ASHA’s Political Campaign Donations and HRC Score Card
Brett Welch, MS, CCC-SLP
6/13/2023
library(tidyverse, quietly = T)
library(readxl)
library(gt)
library(kableExtra)
library(janitor)
library(pdftools)
library(plotly)Data
ASHA-PAC’s Spending
These data were obtained from OpenSecrets.org. Specifically data from ASHA-PAC’s spending on 2020 House and Senate candidates and 2022 House and Senate candidates.
Click here to see ASHA-PAC’s Summary on OpenSecrets.org
Click here to learn about ASHA-PAC and view their Frequently Asked Questions page.
Click here to see ASHA-PAC’s current considerations when deciding who to donate to.
2022
House
asha_2022_rep <- read.csv("/Users/brett/Documents/R/PoliticalSpreadsheet/ASHA_PAC/ASHA_2022_candidateSpending_house.csv")
asha_2022_rep %>% separate(Candidate,
into = c("Name", "ID"),
sep = "\\(",
remove = F) %>%
separate(ID,
into = c("Party", "State"),
sep = "\\-") -> asha_2022_rep
#Clean up state labels
asha_2022_rep %>%
mutate(State = case_when(
State == "Fla)" ~ "FL",
State == "WVa)" ~ "WV",
State == "Mass)" ~ "MA",
State == "NJ)" ~ "NJ",
State == "Calif)" ~ "CA",
State == "Texas)" ~ "TX",
State == "Ky)" ~ "KY",
State == "Wash)" ~ "WA",
State == "NC)" ~ "NC",
State == "Wis)" ~ "WI",
State == "Ill)" ~ "IL",
State == "Va)" ~ "VA",
State == "Pa)" ~ "PA",
State == "NY)" ~ "NY",
State == "Del)" ~ "DE",
State == "Conn)" ~ "CT",
State == "Ore)" ~ "OR",
State == "Mich)" ~ "MI",
State == "Vt)" ~ "VT",
State == "Ohio)" ~ "OH",
State == "Colo)" ~ "CO",
State == "Md)" ~ "MD",
State == "SC)" ~ "SC",
State == "LA)" ~ "LA",
State == "La)" ~ "LA",
State == "Minn)" ~ "MN",
State == "Okla)" ~ "OK",
State == "Mo)" ~ "MO",
State == "Nev)" ~ "NV",
State == "Iowa)" ~ "IA",
State == "Ga)" ~ "GA",
State == "Ark)" ~ "AR",
State == "NH)" ~ "NH",
State == "Ariz)" ~ "AZ",
State == "Ala)" ~ "AL",
State == "Neb)" ~ "NE",
State == "Maine)" ~ "ME",
State == "Miss)" ~ "MS",
State == "NM)" ~ "NM",
State == "Ind)" ~ "IN",
State == "ND)" ~ "ND",
State == "Wyo)" ~ "WY",
State == "Utah)" ~ "UT",
TRUE ~ State
)) -> asha_2022_rep
asha_2022_rep <- asha_2022_rep %>% clean_names()
asha_2022_rep$name <- str_squish(asha_2022_rep$name)
#convert total column to numeric
asha_2022_rep$total <- as.numeric(gsub("\\$", "", asha_2022_rep$total))paste("In the 2022 election cycle, ASHA-PAC donated a total of $", sum(asha_2022_rep$total), "to Representatives' campaigns.")## [1] "In the 2022 election cycle, ASHA-PAC donated a total of $ 126500 to Representatives' campaigns."A list of Representatives ASHA-PAC donated money to for their 2022 campaign
asha_2022_rep %>%
select(-candidate) %>%
kbl(table.attr = "style = \"color: black;\"") %>%
kable_styling(bootstrap_options = c("striped", "hover"), position = "center") %>%
scroll_box(height = "400px")| name | party | state | total |
|---|---|---|---|
| Frank Pallone Jr. | D | NJ | 10000 |
| Cathy McMorris Rodgers | R | WA | 10000 |
| Gus Bilirakis | R | FL | 5000 |
| Rodney Davis | R | IL | 5000 |
| David McKinley | R | WV | 5000 |
| Nancy Pelosi | D | CA | 5000 |
| Mikie Sherrill | D | NJ | 5000 |
| Mike Thompson | D | CA | 5000 |
| Brett Guthrie | R | KY | 4500 |
| Anna Eshoo | D | CA | 4000 |
| Suzanne Bonamici | D | OR | 3500 |
| Joseph Neguse | D | CO | 3500 |
| Lisa Blunt Rochester | D | DE | 3500 |
| Bill Johnson | R | OH | 3000 |
| Cindy Axne | D | IA | 2500 |
| Larry Bucshon | R | IN | 2500 |
| G K Butterfield | D | NC | 2500 |
| Angie Craig | D | MN | 2500 |
| Josh Harder | D | CA | 2500 |
| Doris Matsui | D | CA | 2500 |
| Lauren A Underwood | D | IL | 2500 |
| Alma Adams | D | NC | 1000 |
| Pete Aguilar | D | CA | 1000 |
| Kelly Armstrong | R | ND | 1000 |
| Troy Balderson | R | OH | 1000 |
| Karen Bass | D | CA | 1000 |
| Jaime Herrera Beutler | R | WA | 1000 |
| Earl Blumenauer | D | OR | 1000 |
| Brendan Boyle | D | PA | 1000 |
| Michael Burgess | R | TX | 1000 |
| Liz Cheney | R | WY | 1000 |
| Yvette Clarke | D | NY | 1000 |
| Tom Cole | R | OK | 1000 |
| Joe Courtney | D | CT | 1000 |
| John Curtis | R | UT | 1000 |
| Diana DeGette | D | CO | 1000 |
| Suzan DelBene | D | WA | 1000 |
| Mark Desaulnier | D | CA | 1000 |
| Debbie Dingell | D | MI | 1000 |
| Drew Ferguson | R | GA | 1000 |
| Jimmy Gomez | D | CA | 1000 |
| Kevin Hern | R | OK | 1000 |
| David P Joyce | R | OH | 1000 |
| Robin Kelly | D | IL | 1000 |
| Dan Kildee | D | MI | 1000 |
| Derek Kilmer | D | WA | 1000 |
| Ann Kuster | D | NH | 1000 |
| Darin LaHood | R | IL | 1000 |
| Bob Latta | R | OH | 1000 |
| Lucy McBath | D | GA | 1000 |
| Chris Pappas | D | NH | 1000 |
| Mark Pocan | D | WI | 1000 |
| Jan Schakowsky | D | IL | 1000 |
| Bobby Scott | D | VA | 1000 |
| Terri Sewell | D | AL | 1000 |
| Adrian Smith | R | NE | 1000 |
| Pete Stauber | R | MN | 1000 |
| Tim Walberg | R | MI | 1000 |
| Kevin Brady | R | TX | 0 |
Senate
asha_2022_sen <- read.csv("/Users/brett/Documents/R/PoliticalSpreadsheet/ASHA_PAC/ASHA_2022_candidateSpending_senate.csv")
asha_2022_sen %>% separate(Candidate,
into = c("Name", "ID"),
sep = "\\(",
remove = F) %>%
separate(ID,
into = c("Party", "State"),
sep = "\\-") -> asha_2022_sen
asha_2022_sen %>%
mutate(State = case_when(
State == "Fla)" ~ "FL",
State == "WVa)" ~ "WV",
State == "Mass)" ~ "MA",
State == "NJ)" ~ "NJ",
State == "Calif)" ~ "CA",
State == "Texas)" ~ "TX",
State == "Ky)" ~ "KY",
State == "Wash)" ~ "WA",
State == "NC)" ~ "NC",
State == "Wis)" ~ "WI",
State == "Ill)" ~ "IL",
State == "Va)" ~ "VA",
State == "Pa)" ~ "PA",
State == "NY)" ~ "NY",
State == "Del)" ~ "DE",
State == "Conn)" ~ "CT",
State == "Ore)" ~ "OR",
State == "Mich)" ~ "MI",
State == "Vt)" ~ "VT",
State == "Ohio)" ~ "OH",
State == "Colo)" ~ "CO",
State == "Md)" ~ "MD",
State == "SC)" ~ "SC",
State == "LA)" ~ "LA",
State == "La)" ~ "LA",
State == "Minn)" ~ "MN",
State == "Okla)" ~ "OK",
State == "Mo)" ~ "MO",
State == "Nev)" ~ "NV",
State == "Iowa)" ~ "IA",
State == "Ga)" ~ "GA",
State == "Ark)" ~ "AR",
State == "NH)" ~ "NH",
State == "Ariz)" ~ "AZ",
State == "Ala)" ~ "AL",
State == "Neb)" ~ "NE",
State == "Maine)" ~ "ME",
State == "Miss)" ~ "MS",
State == "NM)" ~ "NM",
State == "Ind)" ~ "IN",
State == "ND)" ~ "ND",
State == "Wyo)" ~ "WY",
State == "Utah)" ~ "UT",
State == "Alaska)" ~ "AK",
State == "SD)" ~ "SD",
TRUE ~ State
)) -> asha_2022_sen
asha_2022_sen <- asha_2022_sen %>%
clean_names()
asha_2022_sen$name <- str_squish(asha_2022_sen$name)
#convert total column to numeric
asha_2022_sen$total <- as.numeric(gsub("\\$", "", asha_2022_sen$total))paste("In the 2022 election cycle, ASHA-PAC donated a total of $", sum(asha_2022_sen$total), "to Senators' campaigns.")## [1] "In the 2022 election cycle, ASHA-PAC donated a total of $ 43000 to Senators' campaigns."A list of Senators that ASHA-PAC donated money to for their 2022 campaign
asha_2022_sen %>%
select(-candidate) %>%
kbl(table.attr = "style = \"color: black;\"") %>%
kable_styling(bootstrap_options = c("striped", "hover"), position = "center") %>%
scroll_box(height = "400px")| name | party | state | total |
|---|---|---|---|
| Patty Murray | D | WA | 6000 |
| Maggie Hassan | D | NH | 5000 |
| Ron Wyden | D | OR | 5000 |
| Lisa Murkowski | R | AK | 4000 |
| Bob Casey | D | PA | 2500 |
| Chuck Grassley | R | IA | 2500 |
| Markwayne Mullin | R | OK | 2500 |
| Charles E Schumer | D | NY | 2500 |
| Tina Smith | D | MN | 2500 |
| John Thune | R | SD | 2500 |
| Susan Collins | R | ME | 2000 |
| Todd Young | R | IN | 2000 |
| John A Barrasso | R | WY | 1000 |
| Sherrod Brown | D | OH | 1000 |
| Cindy Hyde-Smith | R | MS | 1000 |
| Debbie Stabenow | D | MI | 1000 |
2020
House
asha_2020_rep <- read.csv("/Users/brett/Documents/R/PoliticalSpreadsheet/ASHA_PAC/ASHA_2020_candidateSpending_house.csv")
asha_2020_rep %>% separate(Candidate,
into = c("Name", "ID"),
sep = "\\(",
remove = F) %>%
separate(ID,
into = c("Party", "State"),
sep = "\\-") -> asha_2020_rep
#clean up labels for states
asha_2020_rep %>%
mutate(State = case_when(
State == "Fla)" ~ "FL",
State == "WVa)" ~ "WV",
State == "Mass)" ~ "MA",
State == "NJ)" ~ "NJ",
State == "Calif)" ~ "CA",
State == "Texas)" ~ "TX",
State == "Ky)" ~ "KY",
State == "Wash)" ~ "WA",
State == "NC)" ~ "NC",
State == "Wis)" ~ "WI",
State == "Ill)" ~ "IL",
State == "Va)" ~ "VA",
State == "Pa)" ~ "PA",
State == "NY)" ~ "NY",
State == "Del)" ~ "DE",
State == "Conn)" ~ "CT",
State == "Ore)" ~ "OR",
State == "Mich)" ~ "MI",
State == "Vt)" ~ "VT",
State == "Ohio)" ~ "OH",
State == "Colo)" ~ "CO",
State == "Md)" ~ "MD",
State == "SC)" ~ "SC",
State == "LA)" ~ "LA",
State == "Minn)" ~ "MN",
State == "Okla)" ~ "OK",
State == "Mo)" ~ "MO",
State == "Nev)" ~ "NV",
State == "Iowa)" ~ "IA",
State == "Ga)" ~ "GA",
State == "Ark)" ~ "AR",
State == "NH)" ~ "NH",
State == "Ariz)" ~ "AZ",
State == "Ala)" ~ "AL",
State == "Neb)" ~ "NE"
)) -> asha_2020_rep
#unique(asha_2020_rep$State)
asha_2020_rep <- asha_2020_rep %>% clean_names()
asha_2020_rep$name <- str_squish(asha_2020_rep$name)
#convert total column to numeric
asha_2020_rep$total <- as.numeric(gsub("\\$", "", asha_2020_rep$total))paste("In the 2020 election cycle, ASHA-PAC donated a total of $", sum(asha_2020_rep$total), "to Representatives' campaigns.")## [1] "In the 2020 election cycle, ASHA-PAC donated a total of $ 265500 to Representatives' campaigns."A list of Representatives ASHA-PAC donated money to for their 2020 campaign
asha_2020_rep %>%
select(-candidate) %>%
kbl(table.attr = "style = \"color: black;\"") %>%
kable_styling(bootstrap_options = c("striped", "hover"), position = "center") %>%
scroll_box(height = "400px")| name | party | state | total |
|---|---|---|---|
| Gus Bilirakis | R | FL | 10000 |
| David McKinley | R | WV | 10000 |
| Richard E Neal | D | MA | 10000 |
| Frank Pallone Jr. | D | NJ | 10000 |
| Mike Thompson | D | CA | 10000 |
| Kevin Brady | R | TX | 7500 |
| Brett Guthrie | R | KY | 6000 |
| Cathy McMorris Rodgers | R | WA | 6000 |
| Lloyd Doggett | D | TX | 5000 |
| Anna Eshoo | D | CA | 5000 |
| Virginia Foxx | R | NC | 5000 |
| Ron Kind | D | WI | 5000 |
| Doris Matsui | D | CA | 5000 |
| Nancy Pelosi | D | CA | 5000 |
| Bobby L Rush | D | IL | 5000 |
| Bobby Scott | D | VA | 5000 |
| Glenn Thompson | R | PA | 5000 |
| Paul Tonko | D | NY | 5000 |
| Lisa Blunt Rochester | D | DE | 4500 |
| Rosa DeLauro | D | CT | 4000 |
| Donna Shalala | D | FL | 4000 |
| Suzanne Bonamici | D | OR | 3500 |
| Lauren A Underwood | D | IL | 3500 |
| Tim Walberg | R | MI | 3500 |
| Peter Welch | D | VT | 3500 |
| Bill Johnson | R | OH | 3000 |
| Earl Blumenauer | D | OR | 2500 |
| Michael Burgess | R | TX | 2500 |
| G K Butterfield | D | NC | 2500 |
| Diana DeGette | D | CO | 2500 |
| Debbie Dingell | D | MI | 2500 |
| Steny H Hoyer | D | MD | 2500 |
| Robin Kelly | D | IL | 2500 |
| Nita M Lowey | D | NY | 2500 |
| Kevin McCarthy | R | CA | 2500 |
| Tom Rice | R | SC | 2500 |
| Steve Scalise | R | NA | 2500 |
| Elise Stefanik | R | NY | 2500 |
| Fred Upton | R | MI | 2500 |
| Greg Walden | R | OR | 2500 |
| Cheri Bustos | D | IL | 2000 |
| Angie Craig | D | MN | 2000 |
| Rodney Davis | R | IL | 2000 |
| Kendra Horn | D | OK | 2000 |
| David P Joyce | R | OH | 2000 |
| John Larson | D | CT | 2000 |
| Jan Schakowsky | D | IL | 2000 |
| Kurt Schrader | D | OR | 2000 |
| Jason Smith | R | MO | 2000 |
| Steven Horsford | D | NV | 1500 |
| Alma Adams | D | NC | 1000 |
| Cindy Axne | D | IA | 1000 |
| Troy Balderson | R | OH | 1000 |
| Nanette Barragan | D | CA | 1000 |
| Karen Bass | D | CA | 1000 |
| Jaime Herrera Beutler | R | WA | 1000 |
| Brendan Boyle | D | PA | 1000 |
| Anthony Brindisi | D | NY | 1000 |
| Vernon Buchanan | R | FL | 1000 |
| Tony Cardenas | D | CA | 1000 |
| Buddy Carter | R | GA | 1000 |
| Matt Cartwright | D | PA | 1000 |
| Kathy Castor | D | FL | 1000 |
| Judy Chu | D | CA | 1000 |
| Katherine Clark | D | MA | 1000 |
| Yvette D Clarke | D | NY | 1000 |
| James E Clyburn | D | SC | 1000 |
| Tom Cole | R | OK | 1000 |
| Joe Courtney | D | CT | 1000 |
| Danny K Davis | D | IL | 1000 |
| Suzan DelBene | D | WA | 1000 |
| Mark Desaulnier | D | CA | 1000 |
| Mike Doyle | D | PA | 1000 |
| Dwight Evans | D | PA | 1000 |
| Drew Ferguson | R | GA | 1000 |
| Brian Fitzpatrick | R | PA | 1000 |
| Jimmy Gomez | D | CA | 1000 |
| Josh Harder | D | CA | 1000 |
| Jahana Hayes | D | CT | 1000 |
| Brian Higgins | D | NY | 1000 |
| French Hill | R | AR | 1000 |
| George Holding | R | NC | 1000 |
| Jared Huffman | D | CA | 1000 |
| Hakeem Jeffries | D | NY | 1000 |
| Mike Kelly | R | PA | 1000 |
| Dan Kildee | D | MI | 1000 |
| Derek Kilmer | D | WA | 1000 |
| Ann Kuster | D | NH | 1000 |
| Darin LaHood | R | IL | 1000 |
| Bob Latta | R | OH | 1000 |
| John Lewis | D | GA | 1000 |
| Billy Long | R | MO | 1000 |
| Lucy McBath | D | GA | 1000 |
| Donald McEachin | D | VA | 1000 |
| Joseph Neguse | D | CO | 1000 |
| Tom O’Halleran | D | AZ | 1000 |
| Pete Olson | R | TX | 1000 |
| Jimmy Panetta | D | CA | 1000 |
| Chris Pappas | D | NH | 1000 |
| Bill Pascrell Jr. | D | NJ | 1000 |
| Mark Pocan | D | WI | 1000 |
| Tom Reed | R | NY | 1000 |
| Lucille Roybal-Allard | D | CA | 1000 |
| Raul Ruiz | D | CA | 1000 |
| Linda Sanchez | D | CA | 1000 |
| Brad Schneider | D | IL | 1000 |
| Kim Schrier | D | WA | 1000 |
| Terri A Sewell | D | AL | 1000 |
| Adrian Smith | R | NE | 1000 |
| Pete Stauber | R | MN | 1000 |
| Steve Stivers | R | OH | 1000 |
| Tom Suozzi | D | NY | 1000 |
# asha_2020_rep %>% #not a fan of the condensed form
# select(-candidate) %>%
# kbl(table.attr = "style = \"color: black;\"") %>%
# kable_styling(bootstrap_options = c("striped", "hover"), position = "left",
# full_width = F) %>%
# scroll_box(height = "400px")
# datatable(asha_2020_rep, #playing around with different table options
# rownames = F,
# height = "400px",
# class = "display",
# extensions = c("Responsive"))Senate
asha_2020_sen <- read.csv("/Users/brett/Documents/R/PoliticalSpreadsheet/ASHA_PAC/ASHA_2020_candidateSpending_senate.csv")
asha_2020_sen %>% separate(Candidate,
into = c("Name", "ID"),
sep = "\\(",
remove = F) %>%
separate(ID,
into = c("Party", "State"),
sep = "\\-") -> asha_2020_sen
#clean up state labels
asha_2020_sen %>%
mutate(State = case_when(
State == "Fla)" ~ "FL",
State == "WVa)" ~ "WV",
State == "Mass)" ~ "MA",
State == "NJ)" ~ "NJ",
State == "Calif)" ~ "CA",
State == "Texas)" ~ "TX",
State == "Ky)" ~ "KY",
State == "Wash)" ~ "WA",
State == "NC)" ~ "NC",
State == "Wis)" ~ "WI",
State == "Ill)" ~ "IL",
State == "Va)" ~ "VA",
State == "Pa)" ~ "PA",
State == "NY)" ~ "NY",
State == "Del)" ~ "DE",
State == "Conn)" ~ "CT",
State == "Ore)" ~ "OR",
State == "Mich)" ~ "MI",
State == "Vt)" ~ "VT",
State == "Ohio)" ~ "OH",
State == "Colo)" ~ "CO",
State == "Md)" ~ "MD",
State == "SC)" ~ "SC",
State == "LA)" ~ "LA",
State == "La)" ~ "LA",
State == "Minn)" ~ "MN",
State == "Okla)" ~ "OK",
State == "Mo)" ~ "MO",
State == "Nev)" ~ "NV",
State == "Iowa)" ~ "IA",
State == "Ga)" ~ "GA",
State == "Ark)" ~ "AR",
State == "NH)" ~ "NH",
State == "Ariz)" ~ "AZ",
State == "Ala)" ~ "AL",
State == "Neb)" ~ "NE",
State == "Maine)" ~ "ME",
State == "Miss)" ~ "MS",
State == "NM)" ~ "NM",
TRUE ~ State
)) -> asha_2020_sen
# unique(asha_2020_sen$State)
asha_2020_sen <- asha_2020_sen %>% clean_names()
asha_2020_sen$name <- str_squish(asha_2020_sen$name)
#convert total column to numeric
asha_2020_sen$total <- as.numeric(gsub("\\$", "", asha_2020_sen$total))paste("In the 2020 election cycle, ASHA-PAC donated a total of $", sum(asha_2020_sen$total), "to Senators' campaigns.")## [1] "In the 2020 election cycle, ASHA-PAC donated a total of $ 27500 to Senators' campaigns."A list of Senators that ASHA-PAC donated money to for their 2020 campaign.
asha_2020_sen %>%
select(-candidate) %>%
kbl(table.attr = "style = \"color: black;\"") %>%
kable_styling(bootstrap_options = c("striped", "hover"), position = "center") %>%
scroll_box(height = "400px")| name | party | state | total |
|---|---|---|---|
| Bill Cassidy | R | LA | 5000 |
| Susan Collins | R | ME | 3500 |
| Jeanne Shaheen | D | NH | 3500 |
| Dick Durbin | D | IL | 2500 |
| Cindy Hyde-Smith | R | MS | 2500 |
| Ben Ray Lujan | D | NM | 2500 |
| Mark Warner | D | VA | 2500 |
| Jeff Merkley | D | OR | 2000 |
| Shelley Moore Capito | R | WV | 1500 |
| Joe Kennedy III | D | MA | 1000 |
| Tina Smith | D | MN | 1000 |
#Change names across DFs so they match
#anti_join(asha_2022_sen, senators, by = "name")
asha_2022_sen <- asha_2022_sen %>%
mutate(name = case_when(
name == "Charles E Schumer" ~ "Chuck Schumer",
name == "John A Barrasso" ~ "John Barrasso",
TRUE ~ name
)) %>%
rename(ASHA_PAC_total = total) %>%
mutate(year = 2022,
role = "Senator")
#rename total to ASHA_PAC_total, add role (senator/rep) and year for a single DF
asha_2022_rep <- asha_2022_rep %>%
rename(ASHA_PAC_total = total) %>%
mutate(year = 2022,
role = "Representative")
asha_2020_rep <- asha_2020_rep %>%
rename(ASHA_PAC_total = total) %>%
mutate(year = 2020,
role = "Representative")
asha_2020_sen <- asha_2020_sen %>%
rename(ASHA_PAC_total = total) %>%
mutate(year = 2020,
role = "Senator")
#combine information into a single DF
ashaPAC <- bind_rows(asha_2022_rep, asha_2022_sen, asha_2020_rep, asha_2020_sen)
ashaPAC$name <- str_squish(ashaPAC$name) #get rid of extra spacesAt a Glance FINALIZE
ashaPAC %>%
select(-candidate) %>%
filter(year == "2020") %>%
group_by(year, party) %>%
summarise(total_given = sum(ASHA_PAC_total))## # A tibble: 2 × 3
## # Groups: year [1]
## year party total_given
## <dbl> <chr> <dbl>
## 1 2020 D 183000
## 2 2020 R 110000ashaPAC %>%
select(-candidate) %>%
filter(year == "2022") %>%
group_by(year, party) %>%
summarise(total_given = sum(ASHA_PAC_total))## # A tibble: 2 × 3
## # Groups: year [1]
## year party total_given
## <dbl> <chr> <dbl>
## 1 2022 D 102000
## 2 2022 R 67500$169,500 to campaigns in 2022.
ashaPAC %>%
select(-candidate) %>%
group_by(year, party, role) %>%
summarise(total_given = sum(ASHA_PAC_total)) %>%
kbl(table.attr = "style = \"color: black;\"") %>%
kable_styling(bootstrap_options = c("striped", "hover"), position = "center") %>%
scroll_box(height = "400px")| year | party | role | total_given |
|---|---|---|---|
| 2020 | D | Representative | 168000 |
| 2020 | D | Senator | 15000 |
| 2020 | R | Representative | 97500 |
| 2020 | R | Senator | 12500 |
| 2022 | D | Representative | 76500 |
| 2022 | D | Senator | 25500 |
| 2022 | R | Representative | 50000 |
| 2022 | R | Senator | 17500 |
#can probably make a prettier table w/ gt() but not urgent
# ashaPAC %>%
# select(-candidate) %>%
# group_by(year, party, role) %>%
# summarise(total_given = sum(ASHA_PAC_total)) %>%
# gt() HRC Scorecard
Data from HRC’s Scorecard on the 117th, 116th, and 115th Congress are available here.
Code used to scrape data
#Insert file path to the PDF
hrc <- pdf_text("/Users/brett/Documents/R/PoliticalSpreadsheet/ASHA_PAC/117th-Congressional-Scorecard_web.cleaned.pdf")###############################################################
############################ Senate ###########################
###############################################################
################# Page 8
#Separate information into separate columns
#there are 20 votes taken into account
#hrc[8]
senators1 <- hrc[8] #extract page from PDF document
senators1 <- strsplit(senators1, "\n") #Separate strings by new lines
senators1 <- senators1[[1]] #Only extract information we want
#senators1
senators1 <- str_squish(senators1) #remove all excess white space
#senators1
#new easier method
senators1 <- as.data.frame(senators1) #transform into DF
senators1 %>% filter(str_detect(senators1, "\\)")) -> senators1 #filter and only keep rows with ) - keeps all rows with political affiliation (R)/(D)
#senators1[1,]
#one row is retained that is not needed - remove
senators1 <- senators1 %>% filter(senators1 != "SENATOR (Party) SCORE SCORE SCORE A B C D E F G H I J K L M N O P Q R S T")
senators1 %>% separate(senators1, into = c("name", "score1", "score2", "score3", "votes"),
sep = "\\)") -> senators1
#separate votes into different categories
senators1 %>% separate(score1, into = c("score1", "score2", "score3", "score4",
"vote_tuberville",
"vote_johnLewis",
"vote_filibuster",
"vote_womensHealth",
"vote_saferCommunities",
"vote_lee",
"vote_respectForMarriage",
"vote_buttigieg",
"vote_levine",
"vote_jackson",
"vote_robinson",
"vote_nathan",
"vote_impeachment",
"vote_equalityAct",
"vote_DNHA",
"vote_TFPA",
"vote_SSIA",
"vote_IHRDA",
"vote_ECDFA",
"vote_PrEP"),
sep = " ") -> senators1
senators1 <- senators1 %>% select(-score1, -votes) %>%
rename(score1 = score2, score2 = score3, score3 = score4)
############ Page 9 w/ newer method
#hrc[9]
senators2 <- hrc[9]
senators2 <- strsplit(senators2, "\n")
senators2 <- senators2[[1]]
#senators2
senators2 <- str_squish(senators2)
#senators2
#new easier method
senators2 <- as.data.frame(senators2)
#filter out rows that do not contain )
senators2 %>% filter(str_detect(senators2, "\\)")) -> senators2
#senators2 <- senators2[-1,]
#Filter out first row
senators2 <- senators2 %>%
filter(senators2 != "SENATOR (Party) SCORE SCORE SCORE A B C D E F G H I J K L M N O P Q R S T")
senators2 %>% separate(senators2, into = c("name", "score1", "score2", "score3", "votes"),
sep = "\\)") -> senators2
senators2 %>% separate(score1, into = c("score1", "score2", "score3", "score4",
"vote_tuberville",
"vote_johnLewis",
"vote_filibuster",
"vote_womensHealth",
"vote_saferCommunities",
"vote_lee",
"vote_respectForMarriage",
"vote_buttigieg",
"vote_levine",
"vote_jackson",
"vote_robinson",
"vote_nathan",
"vote_impeachment",
"vote_equalityAct",
"vote_DNHA",
"vote_TFPA",
"vote_SSIA",
"vote_IHRDA",
"vote_ECDFA",
"vote_PrEP"),
sep = " ") -> senators2
senators2 <- senators2 %>% select(-score1) %>%
rename(score1 = score2, score2 = score3, score3 = score4) %>%
select(-votes)
hrc_sen <- bind_rows(senators1, senators2)
################ Page 10
#hrc[10]
senators2 <- hrc[10]
senators2 <- strsplit(senators2, "\n")
senators2 <- senators2[[1]]
#senators2
senators2 <- str_squish(senators2)
#senators2
#new easier method
senators2 <- as.data.frame(senators2)
#filter out rows that do not contain )
senators2 %>% filter(str_detect(senators2, "\\)")) -> senators2
#Chuck Schumer's row becomes misaligned due to the footnotes
#Initial value:
#senators2[13,]
# "I LEVINE Schumer, Charles (D) 3, 4 100 100 100 v v v v v v v v v v v v v v N/A N/A N/A v N/A N/A"
#Changing value to remove the "3, 4"
senators2[13,] <- "I LEVINE Schumer, Charles (D) 100 100 100 v v v v v v v v v v v v v v N/A N/A N/A v N/A N/A"
# Robert Casey Jr.'s row also becomse misaligned due to foot notes
#Initial value:
#senators2[24,]
# "R IHRDA Casey Jr., Robert (D) 5 98 97 97 v v v v v v v v v v v J v v j v v v v v"
# Removing the 5
senators2[24,] <- "R IHRDA Casey Jr., Robert (D) 98 97 97 v v v v v v v v v v v J v v j v v v v v"
#senators2 <- senators2[-1,]
#Filter out first row
senators2 <- senators2 %>%
filter(senators2 != "SENATOR (Party) SCORE SCORE SCORE A B C D E F G H I J K L M N O P Q R S T")
senators2 %>% separate(senators2, into = c("name", "score1", "score2", "score3", "votes"),
sep = "\\)") -> senators2
senators2 %>% separate(score1, into = c("score1", "score2", "score3", "score4",
"vote_tuberville",
"vote_johnLewis",
"vote_filibuster",
"vote_womensHealth",
"vote_saferCommunities",
"vote_lee",
"vote_respectForMarriage",
"vote_buttigieg",
"vote_levine",
"vote_jackson",
"vote_robinson",
"vote_nathan",
"vote_impeachment",
"vote_equalityAct",
"vote_DNHA",
"vote_TFPA",
"vote_SSIA",
"vote_IHRDA",
"vote_ECDFA",
"vote_PrEP"),
sep = " ") -> senators2
senators2 <- senators2 %>% select(-score1) %>%
rename(score1 = score2, score2 = score3, score3 = score4) %>%
select(-votes)
hrc_sen <- bind_rows(hrc_sen, senators2)
########## Page 11
#hrc[11]
senators2 <- hrc[11]
senators2 <- strsplit(senators2, "\n")
senators2 <- senators2[[1]]
#senators2
senators2 <- str_squish(senators2)
#senators2
#new easier method
senators2 <- as.data.frame(senators2)
#filter out rows that do not contain )
senators2 %>% filter(str_detect(senators2, "\\)")) -> senators2
senators2 <- senators2 %>%
filter(senators2 != "SENATOR (Party) SCORE SCORE SCORE A B C D E F G H I J K L M N O P Q R S T")
senators2 %>% separate(senators2, into = c("name", "score1", "score2", "score3", "votes"),
sep = "\\)") -> senators2
senators2 %>% separate(score1, into = c("score1", "score2", "score3", "score4",
"vote_tuberville",
"vote_johnLewis",
"vote_filibuster",
"vote_womensHealth",
"vote_saferCommunities",
"vote_lee",
"vote_respectForMarriage",
"vote_buttigieg",
"vote_levine",
"vote_jackson",
"vote_robinson",
"vote_nathan",
"vote_impeachment",
"vote_equalityAct",
"vote_DNHA",
"vote_TFPA",
"vote_SSIA",
"vote_IHRDA",
"vote_ECDFA",
"vote_PrEP"),
sep = " ") -> senators2
senators2 <- senators2 %>% select(-score1) %>%
rename(score1 = score2, score2 = score3, score3 = score4) %>%
select(-votes)
hrc_sen <- bind_rows(hrc_sen, senators2)
###############################################################
###################### House of Representatives ###############
###############################################################
############### Page 17
#hrc[17]
reps1 <- hrc[17] #extract page from PDF document
reps1 <- strsplit(reps1, "\n") #Separate strings by new lines
reps1 <- reps1[[1]] #Only extract information we want
#reps1
reps1 <- str_squish(reps1) #remove all excess white space
#reps1
#new easier method
reps1 <- as.data.frame(reps1) #transform into DF
#Raúl Grijalva's votes not transcribe correctly due to line break
#reps1[21,] = "LESKO AND FOXX/ 3 Grijalva, Raúl (D) 100 100 100"
reps1[21,] <- "LESKO AND FOXX/ 3 Grijalva, Raúl (D) 100 100 100 v v v v v v v v v v v v v v v v v v v v v"
reps1 %>% filter(str_detect(reps1, "\\)")) -> reps1 #filter and only keep rows with ) - keeps all rows with political affiliation (R)/(D)
#reps1[1,]
#one row is retained that is not needed - remove
reps1 <- reps1 %>% filter(reps1 != "REPRESENTATIVE (Party) SCORE SCORE SCORE")
reps1 %>% separate(reps1, into = c("name", "score1", "score2", "score3", "votes"),
sep = "\\)") -> reps1
#separate votes into different categories
reps1 %>% separate(score1, into = c("score1", "score2", "score3", "score4",
"vote_equalityAct",
"vote_AmericanDream",
"vote_backgroundChecks",
"vote_johnLewis",
"vote_violenceAgainstWomen",
"vote_LGBTQ_BECEIA",
"vote_leskoFoxx",
"vote_womensHealth",
"vote_FVPSA",
"vote_globalRespect",
"vote_LGBTQIdataInclusion",
"vote_saferCommunities",
"vote_RMA_first",
"vote_RMA_final",
"vote_impeachment",
"vote_DNHA",
"vote_TFPA",
"vote_SSIA",
"vote_ECDFA",
"vote_PrEP",
"vote_anti-transDischarge"),
sep = " ") -> reps1
reps1 <- reps1 %>% select(-score1, -votes) %>%
rename(score1 = score2, score2 = score3, score3 = score4)
hrc_reps <- reps1
############### Page 18
#hrc[18]
reps1 <- hrc[18] #extract page from PDF document
reps1 <- strsplit(reps1, "\n") #Separate strings by new lines
reps1 <- reps1[[1]] #Only extract information we want
#reps1
reps1 <- str_squish(reps1) #remove all excess white space
#reps1
#new easier method
reps1 <- as.data.frame(reps1) #transform into DF
#### Check and make any changes to lines that were incorrectly transcribed
#reps1[22,] = "LESKO AND FOXX/ 26 Brownley, Julia (D) 100 100 100"
reps1[22,] <- "LESKO AND FOXX/ 26 Brownley, Julia (D) 100 100 100 v v v v v v v v v v v v v v v v v v v v v"
reps1 %>% filter(str_detect(reps1, "\\)")) -> reps1 #filter and only keep rows with ) - keeps all rows with political affiliation (R)/(D)
#reps1[1,]
#one row is retained that is not needed - remove
reps1 <- reps1 %>% filter(reps1 != "REPRESENTATIVE (Party) SCORE SCORE SCORE")
reps1 %>% separate(reps1, into = c("name", "score1", "score2", "score3", "votes"),
sep = "\\)") -> reps1
#separate votes into different categories
reps1 %>% separate(score1, into = c("score1", "score2", "score3", "score4",
"vote_equalityAct",
"vote_AmericanDream",
"vote_backgroundChecks",
"vote_johnLewis",
"vote_violenceAgainstWomen",
"vote_LGBTQ_BECEIA",
"vote_leskoFoxx",
"vote_womensHealth",
"vote_FVPSA",
"vote_globalRespect",
"vote_LGBTQIdataInclusion",
"vote_saferCommunities",
"vote_RMA_first",
"vote_RMA_final",
"vote_impeachment",
"vote_DNHA",
"vote_TFPA",
"vote_SSIA",
"vote_ECDFA",
"vote_PrEP",
"vote_anti-transDischarge"),
sep = " ") -> reps1
reps1 <- reps1 %>% select(-score1, -votes) %>%
rename(score1 = score2, score2 = score3, score3 = score4)
#combine
hrc_reps <- bind_rows(hrc_reps, reps1)
############### Page 19
#hrc[19]
reps1 <- hrc[19] #extract page from PDF document
reps1 <- strsplit(reps1, "\n") #Separate strings by new lines
reps1 <- reps1[[1]] #Only extract information we want
#reps1
reps1 <- str_squish(reps1) #remove all excess white space
#reps1
#new easier method
reps1 <- as.data.frame(reps1) #transform into DF
#### Check and make any changes to lines that were incorrectly transcribed
#reps1[22,] = "LESKO AND FOXX/ 2 Courtney, Joe (D) 100 100 100"
reps1[22,] <- "LESKO AND FOXX/ 2 Courtney, Joe (D) 100 100 100 v v v v v v v v v v v v v v v v v v v v v"
reps1 %>% filter(str_detect(reps1, "\\)")) -> reps1 #filter and only keep rows with ) - keeps all rows with political affiliation (R)/(D)
#reps1[1,]
#one row is retained that is not needed - remove
reps1 <- reps1 %>% filter(reps1 != "REPRESENTATIVE (Party) SCORE SCORE SCORE")
reps1 %>% separate(reps1, into = c("name", "score1", "score2", "score3", "votes"),
sep = "\\)") -> reps1
#separate votes into different categories
reps1 %>% separate(score1, into = c("score1", "score2", "score3", "score4",
"vote_equalityAct",
"vote_AmericanDream",
"vote_backgroundChecks",
"vote_johnLewis",
"vote_violenceAgainstWomen",
"vote_LGBTQ_BECEIA",
"vote_leskoFoxx",
"vote_womensHealth",
"vote_FVPSA",
"vote_globalRespect",
"vote_LGBTQIdataInclusion",
"vote_saferCommunities",
"vote_RMA_first",
"vote_RMA_final",
"vote_impeachment",
"vote_DNHA",
"vote_TFPA",
"vote_SSIA",
"vote_ECDFA",
"vote_PrEP",
"vote_anti-transDischarge"),
sep = " ") -> reps1
reps1 <- reps1 %>% select(-score1, -votes) %>%
rename(score1 = score2, score2 = score3, score3 = score4)
#combine
hrc_reps <- bind_rows(hrc_reps, reps1)
############### Page 20
#hrc[20]
reps1 <- hrc[20] #extract page from PDF document
reps1 <- strsplit(reps1, "\n") #Separate strings by new lines
reps1 <- reps1[[1]] #Only extract information we want
#reps1
reps1 <- str_squish(reps1) #remove all excess white space
#reps1
#new easier method
reps1 <- as.data.frame(reps1) #transform into DF
#### Check and make any changes to lines that were incorrectly transcribed
#reps1[22,] = "LESKO AND FOXX/ 4 Johnson Jr., Henry “Hank” (D) 100 100 100"
reps1[22,] <- "LESKO AND FOXX/ 4 Johnson Jr., Henry “Hank” (D) 100 100 100 v v v v v v v v v v v v v v v v v v v v v"
reps1 %>% filter(str_detect(reps1, "\\)")) -> reps1 #filter and only keep rows with ) - keeps all rows with political affiliation (R)/(D)
#reps1[1,]
#one row is retained that is not needed - remove
reps1 <- reps1 %>% filter(reps1 != "REPRESENTATIVE (Party) SCORE SCORE SCORE")
reps1 %>% separate(reps1, into = c("name", "score1", "score2", "score3", "votes"),
sep = "\\)") -> reps1
#separate votes into different categories
reps1 %>% separate(score1, into = c("score1", "score2", "score3", "score4",
"vote_equalityAct",
"vote_AmericanDream",
"vote_backgroundChecks",
"vote_johnLewis",
"vote_violenceAgainstWomen",
"vote_LGBTQ_BECEIA",
"vote_leskoFoxx",
"vote_womensHealth",
"vote_FVPSA",
"vote_globalRespect",
"vote_LGBTQIdataInclusion",
"vote_saferCommunities",
"vote_RMA_first",
"vote_RMA_final",
"vote_impeachment",
"vote_DNHA",
"vote_TFPA",
"vote_SSIA",
"vote_ECDFA",
"vote_PrEP",
"vote_anti-transDischarge"),
sep = " ") -> reps1
reps1 <- reps1 %>% select(-score1, -votes) %>%
rename(score1 = score2, score2 = score3, score3 = score4)
#combine
hrc_reps <- bind_rows(hrc_reps, reps1)
############### Page 21
#hrc[21]
reps1 <- hrc[21] #extract page from PDF document
reps1 <- strsplit(reps1, "\n") #Separate strings by new lines
reps1 <- reps1[[1]] #Only extract information we want
#reps1
reps1 <- str_squish(reps1) #remove all excess white space
#reps1
#new easier method
reps1 <- as.data.frame(reps1) #transform into DF
#### Check and make any changes to lines that were incorrectly transcribed
#reps1[22,] = "LESKO AND FOXX/ 2 Yakym, Rudy (R)9 N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A"
reps1[22,] <- "LESKO AND FOXX/ 2 Yakym, Rudy (R)9 N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A j N/A j j j j j v"
reps1 %>% filter(str_detect(reps1, "\\)")) -> reps1 #filter and only keep rows with ) - keeps all rows with political affiliation (R)/(D)
#reps1[1,]
#one row is retained that is not needed - remove
reps1 <- reps1 %>% filter(reps1 != "REPRESENTATIVE (Party) SCORE SCORE SCORE")
reps1 %>% separate(reps1, into = c("name", "score1", "score2", "score3", "votes"),
sep = "\\)") -> reps1
#separate votes into different categories
reps1 %>% separate(score1, into = c("score1", "score2", "score3", "score4",
"vote_equalityAct",
"vote_AmericanDream",
"vote_backgroundChecks",
"vote_johnLewis",
"vote_violenceAgainstWomen",
"vote_LGBTQ_BECEIA",
"vote_leskoFoxx",
"vote_womensHealth",
"vote_FVPSA",
"vote_globalRespect",
"vote_LGBTQIdataInclusion",
"vote_saferCommunities",
"vote_RMA_first",
"vote_RMA_final",
"vote_impeachment",
"vote_DNHA",
"vote_TFPA",
"vote_SSIA",
"vote_ECDFA",
"vote_PrEP",
"vote_anti-transDischarge"),
sep = " ") -> reps1
reps1 <- reps1 %>% select(-score1, -votes) %>%
rename(score1 = score2, score2 = score3, score3 = score4)
#combine
hrc_reps <- bind_rows(hrc_reps, reps1)
############### Page 22
#hrc[22]
reps1 <- hrc[22] #extract page from PDF document
reps1 <- strsplit(reps1, "\n") #Separate strings by new lines
reps1 <- reps1[[1]] #Only extract information we want
#reps1
reps1 <- str_squish(reps1) #remove all excess white space
#reps1
#new easier method
reps1 <- as.data.frame(reps1) #transform into DF
#### Check and make any changes to lines that were incorrectly transcribed
#No known issues with this page
#
#
reps1 %>% filter(str_detect(reps1, "\\)")) -> reps1 #filter and only keep rows with ) - keeps all rows with political affiliation (R)/(D)
#reps1[1,]
#one row is retained that is not needed - remove
reps1 <- reps1 %>% filter(reps1 != "REPRESENTATIVE (Party) SCORE SCORE SCORE")
reps1 %>% separate(reps1, into = c("name", "score1", "score2", "score3", "votes"),
sep = "\\)") -> reps1
#separate votes into different categories
reps1 %>% separate(score1, into = c("score1", "score2", "score3", "score4",
"vote_equalityAct",
"vote_AmericanDream",
"vote_backgroundChecks",
"vote_johnLewis",
"vote_violenceAgainstWomen",
"vote_LGBTQ_BECEIA",
"vote_leskoFoxx",
"vote_womensHealth",
"vote_FVPSA",
"vote_globalRespect",
"vote_LGBTQIdataInclusion",
"vote_saferCommunities",
"vote_RMA_first",
"vote_RMA_final",
"vote_impeachment",
"vote_DNHA",
"vote_TFPA",
"vote_SSIA",
"vote_ECDFA",
"vote_PrEP",
"vote_anti-transDischarge"),
sep = " ") -> reps1
reps1 <- reps1 %>% select(-score1, -votes) %>%
rename(score1 = score2, score2 = score3, score3 = score4)
#combine
hrc_reps <- bind_rows(hrc_reps, reps1)
############### Page 23
#hrc[23]
reps1 <- hrc[23] #extract page from PDF document
reps1 <- strsplit(reps1, "\n") #Separate strings by new lines
reps1 <- reps1[[1]] #Only extract information we want
#reps1
reps1 <- str_squish(reps1) #remove all excess white space
#reps1
#new easier method
reps1 <- as.data.frame(reps1) #transform into DF
#### Check and make any changes to lines that were incorrectly transcribed
#reps1[22,] = "LESKO AND FOXX/ 6 Emmer, Tom (R) 14 0 0"
reps1[22,] <- "LESKO AND FOXX/ 6 Emmer, Tom (R) 14 0 0 j j j j j v j j j j j j v v j j j j j j j"
#
#
reps1 %>% filter(str_detect(reps1, "\\)")) -> reps1 #filter and only keep rows with ) - keeps all rows with political affiliation (R)/(D)
#reps1[1,]
#one row is retained that is not needed - remove
reps1 <- reps1 %>% filter(reps1 != "REPRESENTATIVE (Party) SCORE SCORE SCORE")
reps1 %>% separate(reps1, into = c("name", "score1", "score2", "score3", "votes"),
sep = "\\)") -> reps1
#separate votes into different categories
reps1 %>% separate(score1, into = c("score1", "score2", "score3", "score4",
"vote_equalityAct",
"vote_AmericanDream",
"vote_backgroundChecks",
"vote_johnLewis",
"vote_violenceAgainstWomen",
"vote_LGBTQ_BECEIA",
"vote_leskoFoxx",
"vote_womensHealth",
"vote_FVPSA",
"vote_globalRespect",
"vote_LGBTQIdataInclusion",
"vote_saferCommunities",
"vote_RMA_first",
"vote_RMA_final",
"vote_impeachment",
"vote_DNHA",
"vote_TFPA",
"vote_SSIA",
"vote_ECDFA",
"vote_PrEP",
"vote_anti-transDischarge"),
sep = " ") -> reps1
reps1 <- reps1 %>% select(-score1, -votes) %>%
rename(score1 = score2, score2 = score3, score3 = score4)
#combine
hrc_reps <- bind_rows(hrc_reps, reps1)
############### Page 24
#hrc[24]
reps1 <- hrc[24] #extract page from PDF document
reps1 <- strsplit(reps1, "\n") #Separate strings by new lines
reps1 <- reps1[[1]] #Only extract information we want
#reps1
reps1 <- str_squish(reps1) #remove all excess white space
#reps1
#new easier method
reps1 <- as.data.frame(reps1) #transform into DF
#### Check and make any changes to lines that were incorrectly transcribed
#reps1[22,] = "LESKO AND FOXX/ 5 Gottheimer, Josh (D) 100 100 100"
reps1[22,] <- "LESKO AND FOXX/ 5 Gottheimer, Josh (D) 100 100 100 v v v v v v v v v v v v v v v v v v v v v"
#
#
reps1 %>% filter(str_detect(reps1, "\\)")) -> reps1 #filter and only keep rows with ) - keeps all rows with political affiliation (R)/(D)
#reps1[1,]
#one row is retained that is not needed - remove
reps1 <- reps1 %>% filter(reps1 != "REPRESENTATIVE (Party) SCORE SCORE SCORE")
reps1 %>% separate(reps1, into = c("name", "score1", "score2", "score3", "votes"),
sep = "\\)") -> reps1
#separate votes into different categories
reps1 %>% separate(score1, into = c("score1", "score2", "score3", "score4",
"vote_equalityAct",
"vote_AmericanDream",
"vote_backgroundChecks",
"vote_johnLewis",
"vote_violenceAgainstWomen",
"vote_LGBTQ_BECEIA",
"vote_leskoFoxx",
"vote_womensHealth",
"vote_FVPSA",
"vote_globalRespect",
"vote_LGBTQIdataInclusion",
"vote_saferCommunities",
"vote_RMA_first",
"vote_RMA_final",
"vote_impeachment",
"vote_DNHA",
"vote_TFPA",
"vote_SSIA",
"vote_ECDFA",
"vote_PrEP",
"vote_anti-transDischarge"),
sep = " ") -> reps1
reps1 <- reps1 %>% select(-score1, -votes) %>%
rename(score1 = score2, score2 = score3, score3 = score4)
#combine
hrc_reps <- bind_rows(hrc_reps, reps1)
############### Page 25
#hrc[25]
reps1 <- hrc[25] #extract page from PDF document
reps1 <- strsplit(reps1, "\n") #Separate strings by new lines
reps1 <- reps1[[1]] #Only extract information we want
#reps1
reps1 <- str_squish(reps1) #remove all excess white space
#reps1
#new easier method
reps1 <- as.data.frame(reps1) #transform into DF
#### Check and make any changes to lines that were incorrectly transcribed
#reps1[22,] = "LESKO AND FOXX/ 24 Katko, John (R) 68 54 61"
reps1[22,] <- "LESKO AND FOXX/ 24 Katko, John (R) 68 54 61 v j j j v v v j v v v v v v v j j v j j v"
#
#
reps1 %>% filter(str_detect(reps1, "\\)")) -> reps1 #filter and only keep rows with ) - keeps all rows with political affiliation (R)/(D)
#reps1[1,]
#one row is retained that is not needed - remove
reps1 <- reps1 %>% filter(reps1 != "REPRESENTATIVE (Party) SCORE SCORE SCORE")
reps1 %>% separate(reps1, into = c("name", "score1", "score2", "score3", "votes"),
sep = "\\)") -> reps1
#separate votes into different categories
reps1 %>% separate(score1, into = c("score1", "score2", "score3", "score4",
"vote_equalityAct",
"vote_AmericanDream",
"vote_backgroundChecks",
"vote_johnLewis",
"vote_violenceAgainstWomen",
"vote_LGBTQ_BECEIA",
"vote_leskoFoxx",
"vote_womensHealth",
"vote_FVPSA",
"vote_globalRespect",
"vote_LGBTQIdataInclusion",
"vote_saferCommunities",
"vote_RMA_first",
"vote_RMA_final",
"vote_impeachment",
"vote_DNHA",
"vote_TFPA",
"vote_SSIA",
"vote_ECDFA",
"vote_PrEP",
"vote_anti-transDischarge"),
sep = " ") -> reps1
reps1 <- reps1 %>% select(-score1, -votes) %>%
rename(score1 = score2, score2 = score3, score3 = score4)
#combine
hrc_reps <- bind_rows(hrc_reps, reps1)
############### Page 26
#hrc[26]
reps1 <- hrc[26] #extract page from PDF document
reps1 <- strsplit(reps1, "\n") #Separate strings by new lines
reps1 <- reps1[[1]] #Only extract information we want
#reps1
reps1 <- str_squish(reps1) #remove all excess white space
#reps1
#new easier method
reps1 <- as.data.frame(reps1) #transform into DF
#### Check and make any changes to lines that were incorrectly transcribed
#reps1[22,] = "LESKO AND FOXX/ 13 Ryan, Tim (D) 100 100 100"
reps1[22,] <- "LESKO AND FOXX/ 13 Ryan, Tim (D) 100 100 100 v v v v v v v v v v v v v v v v v v v v v"
#
#
reps1 %>% filter(str_detect(reps1, "\\)")) -> reps1 #filter and only keep rows with ) - keeps all rows with political affiliation (R)/(D)
#reps1[1,]
#one row is retained that is not needed - remove
reps1 <- reps1 %>% filter(reps1 != "REPRESENTATIVE (Party) SCORE SCORE SCORE")
reps1 %>% separate(reps1, into = c("name", "score1", "score2", "score3", "votes"),
sep = "\\)") -> reps1
#separate votes into different categories
reps1 %>% separate(score1, into = c("score1", "score2", "score3", "score4",
"vote_equalityAct",
"vote_AmericanDream",
"vote_backgroundChecks",
"vote_johnLewis",
"vote_violenceAgainstWomen",
"vote_LGBTQ_BECEIA",
"vote_leskoFoxx",
"vote_womensHealth",
"vote_FVPSA",
"vote_globalRespect",
"vote_LGBTQIdataInclusion",
"vote_saferCommunities",
"vote_RMA_first",
"vote_RMA_final",
"vote_impeachment",
"vote_DNHA",
"vote_TFPA",
"vote_SSIA",
"vote_ECDFA",
"vote_PrEP",
"vote_anti-transDischarge"),
sep = " ") -> reps1
reps1 <- reps1 %>% select(-score1, -votes) %>%
rename(score1 = score2, score2 = score3, score3 = score4)
#combine
hrc_reps <- bind_rows(hrc_reps, reps1)
############### Page 27
#hrc[27]
reps1 <- hrc[27] #extract page from PDF document
reps1 <- strsplit(reps1, "\n") #Separate strings by new lines
reps1 <- reps1[[1]] #Only extract information we want
#reps1
reps1 <- str_squish(reps1) #remove all excess white space
#reps1
#new easier method
reps1 <- as.data.frame(reps1) #transform into DF
#### Check and make any changes to lines that were incorrectly transcribed
#reps1[21,] = "LESKO AND FOXX/ 1 Cicilline, David (D) 100 100 100"
reps1[21,] <- "LESKO AND FOXX/ 1 Cicilline, David (D) 100 100 100 v v v v v v v v v v v v v v v v v v v v v"
#
#
reps1 %>% filter(str_detect(reps1, "\\)")) -> reps1 #filter and only keep rows with ) - keeps all rows with political affiliation (R)/(D)
#reps1[1,]
#one row is retained that is not needed - remove
reps1 <- reps1 %>% filter(reps1 != "REPRESENTATIVE (Party) SCORE SCORE SCORE")
reps1 %>% separate(reps1, into = c("name", "score1", "score2", "score3", "votes"),
sep = "\\)") -> reps1
#separate votes into different categories
reps1 %>% separate(score1, into = c("score1", "score2", "score3", "score4",
"vote_equalityAct",
"vote_AmericanDream",
"vote_backgroundChecks",
"vote_johnLewis",
"vote_violenceAgainstWomen",
"vote_LGBTQ_BECEIA",
"vote_leskoFoxx",
"vote_womensHealth",
"vote_FVPSA",
"vote_globalRespect",
"vote_LGBTQIdataInclusion",
"vote_saferCommunities",
"vote_RMA_first",
"vote_RMA_final",
"vote_impeachment",
"vote_DNHA",
"vote_TFPA",
"vote_SSIA",
"vote_ECDFA",
"vote_PrEP",
"vote_anti-transDischarge"),
sep = " ") -> reps1
reps1 <- reps1 %>% select(-score1, -votes) %>%
rename(score1 = score2, score2 = score3, score3 = score4)
#combine
hrc_reps <- bind_rows(hrc_reps, reps1)
############### Page 28
#hrc[28]
reps1 <- hrc[28] #extract page from PDF document
reps1 <- strsplit(reps1, "\n") #Separate strings by new lines
reps1 <- reps1[[1]] #Only extract information we want
#reps1
reps1 <- str_squish(reps1) #remove all excess white space
#reps1
#new easier method
reps1 <- as.data.frame(reps1) #transform into DF
#### Check and make any changes to lines that were incorrectly transcribed
#reps1[22,] = "LESKO AND FOXX/ 13 Jackson, Ronny (R) 0 N/A N/A"
reps1[22,] <- "LESKO AND FOXX/ 13 Jackson, Ronny (R) 0 N/A N/A j j j j j j j j j j j j j j j j j j j j j"
#
#
reps1 %>% filter(str_detect(reps1, "\\)")) -> reps1 #filter and only keep rows with ) - keeps all rows with political affiliation (R)/(D)
#reps1[1,]
#one row is retained that is not needed - remove
reps1 <- reps1 %>% filter(reps1 != "REPRESENTATIVE (Party) SCORE SCORE SCORE")
reps1 %>% separate(reps1, into = c("name", "score1", "score2", "score3", "votes"),
sep = "\\)") -> reps1
#separate votes into different categories
reps1 %>% separate(score1, into = c("score1", "score2", "score3", "score4",
"vote_equalityAct",
"vote_AmericanDream",
"vote_backgroundChecks",
"vote_johnLewis",
"vote_violenceAgainstWomen",
"vote_LGBTQ_BECEIA",
"vote_leskoFoxx",
"vote_womensHealth",
"vote_FVPSA",
"vote_globalRespect",
"vote_LGBTQIdataInclusion",
"vote_saferCommunities",
"vote_RMA_first",
"vote_RMA_final",
"vote_impeachment",
"vote_DNHA",
"vote_TFPA",
"vote_SSIA",
"vote_ECDFA",
"vote_PrEP",
"vote_anti-transDischarge"),
sep = " ") -> reps1
reps1 <- reps1 %>% select(-score1, -votes) %>%
rename(score1 = score2, score2 = score3, score3 = score4)
#combine
hrc_reps <- bind_rows(hrc_reps, reps1)
############### Page 29
#hrc[29]
reps1 <- hrc[29] #extract page from PDF document
reps1 <- strsplit(reps1, "\n") #Separate strings by new lines
reps1 <- reps1[[1]] #Only extract information we want
#reps1
reps1 <- str_squish(reps1) #remove all excess white space
#reps1
#new easier method
reps1 <- as.data.frame(reps1) #transform into DF
#### Check and make any changes to lines that were incorrectly transcribed
#reps1[22,] = "LESKO AND FOXX/ 6 Cline, Ben (R) 0 0 N/A"
reps1[22,] <- "LESKO AND FOXX/ 6 Cline, Ben (R) 0 0 N/A j j j j j j j j j j j j j j j j j j j j j"
#
#
reps1 %>% filter(str_detect(reps1, "\\)")) -> reps1 #filter and only keep rows with ) - keeps all rows with political affiliation (R)/(D)
#reps1[1,]
#one row is retained that is not needed - remove
reps1 <- reps1 %>% filter(reps1 != "REPRESENTATIVE (Party) SCORE SCORE SCORE")
reps1 %>% separate(reps1, into = c("name", "score1", "score2", "score3", "votes"),
sep = "\\)") -> reps1
#separate votes into different categories
reps1 %>% separate(score1, into = c("score1", "score2", "score3", "score4",
"vote_equalityAct",
"vote_AmericanDream",
"vote_backgroundChecks",
"vote_johnLewis",
"vote_violenceAgainstWomen",
"vote_LGBTQ_BECEIA",
"vote_leskoFoxx",
"vote_womensHealth",
"vote_FVPSA",
"vote_globalRespect",
"vote_LGBTQIdataInclusion",
"vote_saferCommunities",
"vote_RMA_first",
"vote_RMA_final",
"vote_impeachment",
"vote_DNHA",
"vote_TFPA",
"vote_SSIA",
"vote_ECDFA",
"vote_PrEP",
"vote_anti-transDischarge"),
sep = " ") -> reps1
reps1 <- reps1 %>% select(-score1, -votes) %>%
rename(score1 = score2, score2 = score3, score3 = score4)
#combine
hrc_reps <- bind_rows(hrc_reps, reps1)
############### Page 30
#hrc[30]
reps1 <- hrc[30] #extract page from PDF document
reps1 <- strsplit(reps1, "\n") #Separate strings by new lines
reps1 <- reps1[[1]] #Only extract information we want
#reps1
reps1 <- str_squish(reps1) #remove all excess white space
#Removing the extra white space reduces the votes for the Delegates from the territories
#reps1
#new easier method
reps1 <- as.data.frame(reps1) #transform into DF
#### Check and make any changes to lines that were incorrectly transcribed
#reps1[22,] = "LESKO AND FOXX/ 6 Cline, Ben (R) 0 0 N/A"
# reps1[22,] <- "LESKO AND FOXX/ 6 Cline, Ben (R) 0 0 N/A j j j j j j j j j j j j j j j j j j j j j"
#
#
reps1 %>% filter(str_detect(reps1, "\\)")) -> reps1 #filter and only keep rows with ) - keeps all rows with political affiliation (R)/(D)
#reps1[1,]
#one row is retained that is not needed - remove
reps1 <- reps1 %>% filter(reps1 != "REPRESENTATIVE (Party) SCORE SCORE SCORE")
reps1 %>% separate(reps1, into = c("name", "score1", "score2", "score3", "votes"),
sep = "\\)") -> reps1
#separate votes into different categories
reps1 %>% separate(score1, into = c("score1", "score2", "score3", "score4",
"vote_equalityAct",
"vote_AmericanDream",
"vote_backgroundChecks",
"vote_johnLewis",
"vote_violenceAgainstWomen",
"vote_LGBTQ_BECEIA",
"vote_leskoFoxx",
"vote_womensHealth",
"vote_FVPSA",
"vote_globalRespect",
"vote_LGBTQIdataInclusion",
"vote_saferCommunities",
"vote_RMA_first",
"vote_RMA_final",
"vote_impeachment",
"vote_DNHA",
"vote_TFPA",
"vote_SSIA",
"vote_ECDFA",
"vote_PrEP",
"vote_anti-transDischarge"),
sep = " ") -> reps1
reps1 <- reps1 %>% select(-score1, -votes) %>%
rename(score1 = score2, score2 = score3, score3 = score4)
#Fix the voting records for Territory Delegates
reps1 <- reps1 %>%
mutate(vote_equalityAct = case_when( #change values to NA for Equality Act
name == "AL Amata Coleman (R" ~ NA_character_,
name == "O IMPEACHMENT AL Norton, Eleanor Holmes (D" ~ NA_character_,
name == "P DNHA AL San Nicolas, Michael F.Q. (D" ~ NA_character_,
name == "Q TFPA AL Sablan, Gregorio (D" ~ NA_character_,
name == "R SSIA AL González-Colón, Jenniffer (R" ~ NA_character_,
name == "AL Plaskett, Stacey (D" ~ NA_character_,
TRUE ~ vote_equalityAct
),
#American Dream
vote_AmericanDream = case_when(
name == "AL Amata Coleman (R" ~ NA_character_,
name == "O IMPEACHMENT AL Norton, Eleanor Holmes (D" ~ NA_character_,
name == "P DNHA AL San Nicolas, Michael F.Q. (D" ~ NA_character_,
name == "Q TFPA AL Sablan, Gregorio (D" ~ NA_character_,
name == "R SSIA AL González-Colón, Jenniffer (R" ~ NA_character_,
name == "AL Plaskett, Stacey (D" ~ NA_character_,
TRUE ~ vote_AmericanDream
),
#Background checks
vote_backgroundChecks = case_when(
name == "AL Amata Coleman (R" ~ NA_character_,
name == "O IMPEACHMENT AL Norton, Eleanor Holmes (D" ~ NA_character_,
name == "P DNHA AL San Nicolas, Michael F.Q. (D" ~ NA_character_,
name == "Q TFPA AL Sablan, Gregorio (D" ~ NA_character_,
name == "R SSIA AL González-Colón, Jenniffer (R" ~ NA_character_,
name == "AL Plaskett, Stacey (D" ~ NA_character_,
TRUE ~ vote_backgroundChecks
),
#vote John Lewis
vote_johnLewis = case_when(
name == "AL Amata Coleman (R" ~ NA_character_,
name == "O IMPEACHMENT AL Norton, Eleanor Holmes (D" ~ NA_character_,
name == "P DNHA AL San Nicolas, Michael F.Q. (D" ~ NA_character_,
name == "Q TFPA AL Sablan, Gregorio (D" ~ NA_character_,
name == "R SSIA AL González-Colón, Jenniffer (R" ~ NA_character_,
name == "AL Plaskett, Stacey (D" ~ NA_character_,
TRUE ~ vote_johnLewis
),
#violence against women
vote_violenceAgainstWomen = case_when(
name == "AL Amata Coleman (R" ~ NA_character_,
name == "O IMPEACHMENT AL Norton, Eleanor Holmes (D" ~ NA_character_,
name == "P DNHA AL San Nicolas, Michael F.Q. (D" ~ NA_character_,
name == "Q TFPA AL Sablan, Gregorio (D" ~ NA_character_,
name == "R SSIA AL González-Colón, Jenniffer (R" ~ NA_character_,
name == "AL Plaskett, Stacey (D" ~ NA_character_,
TRUE ~ vote_violenceAgainstWomen
),
#LGBTQIA BECEIA
vote_LGBTQ_BECEIA = case_when(
name == "AL Amata Coleman (R" ~ NA_character_,
name == "O IMPEACHMENT AL Norton, Eleanor Holmes (D" ~ NA_character_,
name == "P DNHA AL San Nicolas, Michael F.Q. (D" ~ NA_character_,
name == "Q TFPA AL Sablan, Gregorio (D" ~ NA_character_,
name == "R SSIA AL González-Colón, Jenniffer (R" ~ NA_character_,
name == "AL Plaskett, Stacey (D" ~ NA_character_,
TRUE ~ vote_LGBTQ_BECEIA
),
#Lesko Foxx
vote_leskoFoxx = case_when(
name == "AL Amata Coleman (R" ~ NA_character_,
name == "O IMPEACHMENT AL Norton, Eleanor Holmes (D" ~ NA_character_,
name == "P DNHA AL San Nicolas, Michael F.Q. (D" ~ NA_character_,
name == "Q TFPA AL Sablan, Gregorio (D" ~ NA_character_,
name == "R SSIA AL González-Colón, Jenniffer (R" ~ NA_character_,
name == "AL Plaskett, Stacey (D" ~ NA_character_,
TRUE ~ vote_leskoFoxx
),
#women's health
vote_womensHealth = case_when(
name == "AL Amata Coleman (R" ~ NA_character_,
name == "O IMPEACHMENT AL Norton, Eleanor Holmes (D" ~ NA_character_,
name == "P DNHA AL San Nicolas, Michael F.Q. (D" ~ NA_character_,
name == "Q TFPA AL Sablan, Gregorio (D" ~ NA_character_,
name == "R SSIA AL González-Colón, Jenniffer (R" ~ NA_character_,
name == "AL Plaskett, Stacey (D" ~ NA_character_,
TRUE ~ vote_womensHealth
),
#FVPSA
vote_FVPSA = case_when(
name == "AL Amata Coleman (R" ~ NA_character_,
name == "O IMPEACHMENT AL Norton, Eleanor Holmes (D" ~ NA_character_,
name == "P DNHA AL San Nicolas, Michael F.Q. (D" ~ NA_character_,
name == "Q TFPA AL Sablan, Gregorio (D" ~ NA_character_,
name == "R SSIA AL González-Colón, Jenniffer (R" ~ NA_character_,
name == "AL Plaskett, Stacey (D" ~ NA_character_,
TRUE ~ vote_FVPSA
),
#Global Respect
vote_globalRespect = case_when(
name == "AL Amata Coleman (R" ~ NA_character_,
name == "O IMPEACHMENT AL Norton, Eleanor Holmes (D" ~ NA_character_,
name == "P DNHA AL San Nicolas, Michael F.Q. (D" ~ NA_character_,
name == "Q TFPA AL Sablan, Gregorio (D" ~ NA_character_,
name == "R SSIA AL González-Colón, Jenniffer (R" ~ NA_character_,
name == "AL Plaskett, Stacey (D" ~ NA_character_,
TRUE ~ vote_globalRespect
),
#LGBTQIA Data Inclusion
vote_LGBTQIdataInclusion = case_when(
name == "AL Amata Coleman (R" ~ NA_character_,
name == "O IMPEACHMENT AL Norton, Eleanor Holmes (D" ~ NA_character_,
name == "P DNHA AL San Nicolas, Michael F.Q. (D" ~ NA_character_,
name == "Q TFPA AL Sablan, Gregorio (D" ~ NA_character_,
name == "R SSIA AL González-Colón, Jenniffer (R" ~ NA_character_,
name == "AL Plaskett, Stacey (D" ~ NA_character_,
TRUE ~ vote_LGBTQIdataInclusion
),
#Safter Communities
vote_saferCommunities = case_when(
name == "AL Amata Coleman (R" ~ NA_character_,
name == "O IMPEACHMENT AL Norton, Eleanor Holmes (D" ~ NA_character_,
name == "P DNHA AL San Nicolas, Michael F.Q. (D" ~ NA_character_,
name == "Q TFPA AL Sablan, Gregorio (D" ~ NA_character_,
name == "R SSIA AL González-Colón, Jenniffer (R" ~ NA_character_,
name == "AL Plaskett, Stacey (D" ~ NA_character_,
TRUE ~ vote_saferCommunities
),
#RMA first
vote_RMA_first = case_when(
name == "AL Amata Coleman (R" ~ NA_character_,
name == "O IMPEACHMENT AL Norton, Eleanor Holmes (D" ~ NA_character_,
name == "P DNHA AL San Nicolas, Michael F.Q. (D" ~ NA_character_,
name == "Q TFPA AL Sablan, Gregorio (D" ~ NA_character_,
name == "R SSIA AL González-Colón, Jenniffer (R" ~ NA_character_,
name == "AL Plaskett, Stacey (D" ~ NA_character_,
TRUE ~ vote_RMA_first
),
#RMA Final
vote_RMA_final = case_when(
name == "AL Amata Coleman (R" ~ NA_character_,
name == "O IMPEACHMENT AL Norton, Eleanor Holmes (D" ~ NA_character_,
name == "P DNHA AL San Nicolas, Michael F.Q. (D" ~ NA_character_,
name == "Q TFPA AL Sablan, Gregorio (D" ~ NA_character_,
name == "R SSIA AL González-Colón, Jenniffer (R" ~ NA_character_,
name == "AL Plaskett, Stacey (D" ~ NA_character_,
TRUE ~ vote_RMA_final
),
#Impeachment
vote_impeachment = case_when(
name == "AL Amata Coleman (R" ~ NA_character_,
name == "O IMPEACHMENT AL Norton, Eleanor Holmes (D" ~ NA_character_,
name == "P DNHA AL San Nicolas, Michael F.Q. (D" ~ NA_character_,
name == "Q TFPA AL Sablan, Gregorio (D" ~ NA_character_,
name == "R SSIA AL González-Colón, Jenniffer (R" ~ NA_character_,
name == "AL Plaskett, Stacey (D" ~ NA_character_,
TRUE ~ vote_impeachment
),
#DNHA
vote_DNHA = case_when(
name == "AL Amata Coleman (R" ~ "j",
name == "O IMPEACHMENT AL Norton, Eleanor Holmes (D" ~ "v",
name == "P DNHA AL San Nicolas, Michael F.Q. (D" ~ "j",
name == "Q TFPA AL Sablan, Gregorio (D" ~ "v",
name == "R SSIA AL González-Colón, Jenniffer (R" ~ "j",
name == "AL Plaskett, Stacey (D" ~ "j",
TRUE ~ vote_DNHA
),
#TFPA
vote_TFPA = case_when(
name == "AL Amata Coleman (R" ~ "j",
name == "O IMPEACHMENT AL Norton, Eleanor Holmes (D" ~ "v",
name == "P DNHA AL San Nicolas, Michael F.Q. (D" ~ "j",
name == "Q TFPA AL Sablan, Gregorio (D" ~ "v",
name == "R SSIA AL González-Colón, Jenniffer (R" ~ "j",
name == "AL Plaskett, Stacey (D" ~ "j",
TRUE ~ vote_TFPA
),
#SSIA
vote_SSIA = case_when(
name == "AL Amata Coleman (R" ~ "j",
name == "O IMPEACHMENT AL Norton, Eleanor Holmes (D" ~ "v",
name == "P DNHA AL San Nicolas, Michael F.Q. (D" ~ "v",
name == "Q TFPA AL Sablan, Gregorio (D" ~ "j",
name == "R SSIA AL González-Colón, Jenniffer (R" ~ "j",
name == "AL Plaskett, Stacey (D" ~ "j",
TRUE ~ vote_SSIA
),
#ECDFA
vote_ECDFA = case_when(
name == "AL Amata Coleman (R" ~ "j",
name == "O IMPEACHMENT AL Norton, Eleanor Holmes (D" ~ "v",
name == "P DNHA AL San Nicolas, Michael F.Q. (D" ~ "v",
name == "Q TFPA AL Sablan, Gregorio (D" ~ "v",
name == "R SSIA AL González-Colón, Jenniffer (R" ~ "v",
name == "AL Plaskett, Stacey (D" ~ "v",
TRUE ~ vote_ECDFA
),
#PrEP Access
vote_PrEP = case_when(
name == "AL Amata Coleman (R" ~ "j",
name == "O IMPEACHMENT AL Norton, Eleanor Holmes (D" ~ "v",
name == "P DNHA AL San Nicolas, Michael F.Q. (D" ~ "v",
name == "Q TFPA AL Sablan, Gregorio (D" ~ "v",
name == "R SSIA AL González-Colón, Jenniffer (R" ~ "j",
name == "AL Plaskett, Stacey (D" ~ "j",
TRUE ~ vote_PrEP
),
#Anti-trans discharge
`vote_anti-transDischarge` = case_when(
name == "AL Amata Coleman (R" ~ NA_character_,
name == "O IMPEACHMENT AL Norton, Eleanor Holmes (D" ~ NA_character_,
name == "P DNHA AL San Nicolas, Michael F.Q. (D" ~ NA_character_,
name == "Q TFPA AL Sablan, Gregorio (D" ~ NA_character_,
name == "R SSIA AL González-Colón, Jenniffer (R" ~ NA_character_,
name == "AL Plaskett, Stacey (D" ~ NA_character_,
TRUE ~ `vote_anti-transDischarge`
))
#combine
hrc_reps <- bind_rows(hrc_reps, reps1)Here are the members of the 117th Congress and their HRC scores on equality.
Representatives
hrc_reps <- hrc_reps %>%
rename(score_117 = score1,
score_116 = score2,
score_115 = score3)
#CHANGE THE CHARACTERS RE VOTING
hrc_reps %>% mutate_at(vars(5:25),
funs(case_when(
. == "j" ~ "anti-HRC",
. =="v" ~ "pro-HRC",
. == "J" ~ "did not vote",
. == "P" ~ "voted present",
TRUE ~ .
))) -> hrc_reps
#clean up names
hrc_reps %>% separate(name, into = c("Name", "ID"),
sep = "\\(") %>%
separate(Name, into = c("spot1", "spot2", "spot3", "spot4", "spot5",
"spot6", "spot7","spot8","spot9","spot10"),
sep = "[[:blank:]]") %>%
filter(str_detect(spot2, "\\,")) %>%
select(-spot1, -spot4:-spot10 )%>%
rename(last_name = spot2, first_name = spot3) -> hrc_rep_df
hrc_reps %>% separate(name, into = c("Name", "ID"),
sep = "\\(") %>%
separate(Name, into = c("spot1", "spot2", "spot3", "spot4", "spot5",
"spot6", "spot7","spot8","spot9","spot10"),
sep = "[[:blank:]]") %>%
filter(str_detect(spot3, "\\,")) %>%
select(-spot1) %>%
mutate(spot2 = case_when(
spot2 == "13" ~ NA_character_,
spot2 == "51" ~ NA_character_,
spot2 == "19" ~ NA_character_,
spot2 == "8" ~ NA_character_,
spot2 == "11" ~ NA_character_,
spot2 == "7" ~ NA_character_,
TRUE ~ spot2
)) %>%
unite("last_name", spot2, spot3, sep = " ", na.rm= T) %>%
rename(first_name = spot4) %>%
select(-spot5:-spot10) -> hrc_rep_df2
hrc_rep_df <- bind_rows(hrc_rep_df, hrc_rep_df2)
hrc_reps %>% separate(name, into = c("Name", "ID"),
sep = "\\(") %>%
separate(Name, into = c("spot1", "spot2", "spot3", "spot4", "spot5",
"spot6", "spot7","spot8","spot9","spot10"),
sep = "[[:blank:]]") %>%
filter(str_detect(spot4, "\\,")) %>%
select(-spot1:-spot3) %>%
rename(last_name = spot4) %>%
unite("first_name", spot5, spot6, sep = " ", na.rm = T) %>%
select(-spot7:-spot10) -> hrc_rep_df2
hrc_rep_df <- bind_rows(hrc_rep_df, hrc_rep_df2)
hrc_reps %>% separate(name, into = c("Name", "ID"),
sep = "\\(") %>%
separate(Name, into = c("spot1", "spot2", "spot3", "spot4", "spot5",
"spot6", "spot7","spot8","spot9","spot10"),
sep = "[[:blank:]]") %>%
filter(str_detect(spot5, "\\,")) %>%
select(-spot1:-spot3) %>%
mutate(spot4 = case_when(
spot4 %in% c("1","2","3","4","5","6","9","10","12","13","14","15","24",
"26", "34", "36", "53", "21", "23") ~ NA_character_,
TRUE ~ spot4
)) %>%
unite("last_name", spot4, spot5, sep = " ", na.rm = T) %>%
rename(first_name = spot6) %>%
select(-spot7:-spot10) -> hrc_rep_df2
hrc_rep_df <- bind_rows(hrc_rep_df, hrc_rep_df2)
hrc_reps %>% separate(name, into = c("Name", "ID"),
sep = "\\(") %>%
separate(Name, into = c("spot1", "spot2", "spot3", "spot4", "spot5",
"spot6", "spot7","spot8","spot9","spot10"),
sep = "[[:blank:]]") %>%
filter(str_detect(spot6, "\\,")) %>%
select(-spot1:-spot4) %>%
mutate(spot5 = case_when(
spot5 %in% c("1","2","3","4","5","6","9","10","12","13","14","15","24",
"26","34","36","53","21","23","11","17","AL","25","19",
"37", "39","7") ~ NA_character_,
TRUE ~ spot5
)) %>%
unite("last_name", spot5, spot6, sep = " ", na.rm = T) %>%
rename(first_name = spot7) %>%
select(-spot8:-spot10) -> hrc_rep_df2
hrc_rep_df <- bind_rows(hrc_rep_df, hrc_rep_df2)
hrc_reps %>% separate(name, into = c("Name", "ID"),
sep = "\\(") %>%
separate(Name, into = c("spot1", "spot2", "spot3", "spot4", "spot5",
"spot6", "spot7","spot8","spot9","spot10"),
sep = "[[:blank:]]") %>%
filter(str_detect(spot7, "\\,")) %>%
select(-spot1:-spot5) %>%
unite("last_name", spot6, spot7, sep = " ") %>%
select(-spot9:-spot10)%>%
rename(first_name = spot8) -> hrc_rep_df2
hrc_rep_df <- bind_rows(hrc_rep_df, hrc_rep_df2)
hrc_rep_df %>% unite("name", first_name, last_name, sep = " ", remove = F) %>%
mutate(name = str_replace(name, "[[:punct:]]", "")) -> hrc_rep_df
#clean up the names and remove additional spaces
hrc_rep_df$name <- str_squish(hrc_rep_df$name)
#Change columns to numeric and not characters
hrc_rep_df$score_117 <- as.numeric(hrc_rep_df$score_117)
hrc_rep_df$score_116 <- as.numeric(hrc_rep_df$score_116)
hrc_rep_df$score_115 <- as.numeric(hrc_rep_df$score_115)#calculate their average score based on previous voting record
hrc_rep_df %>%
select(-last_name, - first_name) %>% #drop columns for tidiness
pivot_longer(score_117:score_115, names_to = "session", values_to = "score") %>%
group_by(name) %>% #group reps
summarise(name = name,
avg_score = round(mean(score, na.rm = T),2)) %>%
unique() %>% #drop duplicates
ungroup() %>%
right_join(hrc_rep_df, by = "name") -> hrc_rep_dfThe table below shows the House of Representatives and their scores of equality based on their voting record (when available). The column avg_score calculate the average of the available equality scores from the HRC. Not all Representatives served in prior congressional sessions, which is reflected by “NA” and does not count towards the avg_score.
#print table
hrc_rep_df %>%
select(name, ID, avg_score, score_117, score_116, score_115) %>%
arrange(name)%>%
kbl(table.attr = "style = \"color: black;\"") %>%
kable_styling(bootstrap_options = c("striped", "hover"), position = "center") %>%
scroll_box(height = "400px")| name | ID | avg_score | score_117 | score_116 | score_115 |
|---|---|---|---|---|---|
| Abigail Spanberger | D | 100.00 | 100 | 100 | NA |
| Adam Kinzinger | R | 24.67 | 60 | 11 | 3 |
| Adam Schiff | D | 100.00 | 100 | 100 | 100 |
| Adam Smith | D | 100.00 | 100 | 100 | 100 |
| Adrian Smith | R | 0.00 | 0 | 0 | 0 |
| Adriano Espaillat | D | 100.00 | 100 | 100 | 100 |
| Al Green | D | 95.00 | 94 | 100 | 91 |
| Al Lawson | D | 98.67 | 96 | 100 | 100 |
| Alan Lowenthal | D | 100.00 | 100 | 100 | 100 |
| Albio Sires | D | 99.33 | 98 | 100 | 100 |
| Alex Mooney | R | 0.00 | 0 | 0 | 0 |
| Alexandria OcasioCortez, | D | 100.00 | 100 | 100 | NA |
| Alma Adams | D | 98.00 | 94 | 100 | 100 |
| Ami Bera | D | 100.00 | 100 | 100 | 100 |
| André Carson | D | 100.00 | 100 | 100 | 100 |
| Andrew Clyde | R | 0.00 | 0 | NA | NA |
| Andrew Garbarino | R | 26.00 | 26 | NA | NA |
| Andy Barr | R | 0.00 | 0 | 0 | 0 |
| Andy Biggs | R | 5.00 | 0 | 0 | 15 |
| Andy Harris | R | 0.00 | 0 | 0 | 0 |
| Andy Kim | D | 100.00 | 100 | 100 | NA |
| Andy Levin | D | 97.00 | 94 | 100 | NA |
| Angie Craig | D | 100.00 | 100 | 100 | NA |
| Ann Kirkpatrick | D | 100.00 | 100 | 100 | NA |
| Ann Kuster | D | 100.00 | 100 | 100 | 100 |
| Ann Wagner | R | 5.00 | 8 | 7 | 0 |
| Anna Eshoo | D | 99.33 | 98 | 100 | 100 |
| Anthony Brown | D | 100.00 | 100 | 100 | 100 |
| Anthony Gonzalez | R | 28.00 | 49 | 7 | NA |
| Ashley Hinson | R | 14.00 | 14 | NA | NA |
| August Pfluger | R | 0.00 | 0 | NA | NA |
| Austin Scott | R | 0.00 | 0 | 0 | 0 |
| Ayanna Pressley | D | 100.00 | 100 | 100 | NA |
| Barbara Lee | D | 100.00 | 100 | 100 | 100 |
| Barry Loudermilk | R | 0.00 | 0 | 0 | 0 |
| Barry Moore | R | 0.00 | 0 | NA | NA |
| Ben Cline | R | 0.00 | 0 | 0 | NA |
| Bennie Thompson | D | 98.00 | 94 | 100 | 100 |
| Beth Van Duyne | R | 0.00 | 0 | NA | NA |
| Betty McCollum | D | 100.00 | 100 | 100 | 100 |
| Bill Foster | D | 100.00 | 100 | 100 | 100 |
| Bill Huizenga | R | 0.00 | 0 | 0 | 0 |
| Bill Johnson | R | 0.00 | 0 | 0 | 0 |
| Bill Keating | D | 99.33 | 98 | 100 | 100 |
| Bill Pascrell Jr, | D | 100.00 | 100 | 100 | 100 |
| Bill Posey | R | 0.00 | 0 | 0 | 0 |
| Billy Long | R | 0.00 | 0 | 0 | 0 |
| Blaine Luetkemeyer | R | 0.00 | 0 | 0 | 0 |
| Blake Moore | R | 8.00 | 8 | NA | NA |
| Bob Gibbs | R | 0.00 | 0 | 0 | 0 |
| Bob Good | R | 0.00 | 0 | NA | NA |
| Bobby Rush | D | 98.67 | 96 | 100 | 100 |
| Bonnie Watson Coleman | D | 100.00 | 100 | 100 | 100 |
| Brad Finstad | R | NaN | NA | NA | NA |
| Brad Schneider | D | 100.00 | 100 | 100 | 100 |
| Brad Sherman | D | 100.00 | 100 | 100 | 100 |
| Brad Wenstrup | R | 0.00 | 0 | 0 | 0 |
| Brenda Lawrence | D | 100.00 | 100 | 100 | 100 |
| Brendan Boyle | D | 100.00 | 100 | 100 | 100 |
| Brett Guthrie | R | 0.00 | 0 | 0 | 0 |
| Brian Babin | R | 0.00 | 0 | 0 | 0 |
| Brian Fitzpatrick | R | 66.67 | 68 | 71 | 61 |
| Brian Higgins | D | 100.00 | 100 | 100 | 100 |
| Brian Mast | R | 3.67 | 4 | 7 | 0 |
| Bruce Westerman | R | 0.00 | 0 | 0 | 0 |
| Bryan Steil | R | 10.50 | 14 | 7 | NA |
| Buddy Carter | R | 0.00 | 0 | 0 | 0 |
| Burgess Owens | R | 4.00 | 4 | NA | NA |
| Byron Donalds | R | 0.00 | 0 | NA | NA |
| C Franklin, | R | 0.00 | 0 | NA | NA |
| Carlos Gimenez | R | 32.00 | 32 | NA | NA |
| Carol Miller | R | 0.00 | 0 | 0 | NA |
| Carolyn Bourdeaux | D | 98.00 | 98 | NA | NA |
| Carolyn Maloney | D | 100.00 | 100 | 100 | 100 |
| Cathy McMorris Rodgers | R | 0.00 | 0 | 0 | 0 |
| Chellie Pingree | D | 100.00 | 100 | 100 | 100 |
| Cheri Bustos | D | 100.00 | 100 | 100 | 100 |
| Chip Roy | R | 0.00 | 0 | 0 | NA |
| Chris Jacobs | R | 20.00 | 20 | NA | NA |
| Chris Pappas | D | 100.00 | 100 | 100 | NA |
| Chris Smith | R | 15.00 | 13 | 17 | 15 |
| Chris Stewart | R | 4.33 | 10 | 3 | 0 |
| Chrissy Houlahan | D | 99.00 | 98 | 100 | NA |
| Chuck Fleischmann | R | 0.00 | 0 | 0 | 0 |
| Cindy Axne | D | 96.50 | 96 | 97 | NA |
| Claudia Tenney | R | 0.00 | 0 | NA | NA |
| Clay Higgins | R | 0.00 | 0 | 0 | 0 |
| Cliff Bentz | R | 4.00 | 4 | NA | NA |
| Colin Allred | D | 98.50 | 100 | 97 | NA |
| Connie Conway | R | NaN | NA | NA | NA |
| Conor Lamb | D | 95.00 | 98 | 94 | 93 |
| Cori Bush | D | 100.00 | 100 | NA | NA |
| Dan Bishop | R | 0.00 | 0 | NA | NA |
| Dan Crenshaw | R | 0.00 | 0 | 0 | NA |
| Dan Kildee | D | 100.00 | 100 | 100 | 100 |
| Dan Meuser | R | 2.00 | 4 | 0 | NA |
| Dan Newhouse | R | 11.00 | 26 | 7 | 0 |
| Daniel Webster | R | 0.00 | 0 | 0 | 0 |
| Danny Davis | D | 95.00 | 100 | 97 | 88 |
| Darin LaHood | R | 0.00 | 0 | 0 | 0 |
| Darrell Issa | R | 19.50 | 14 | NA | 25 |
| Darren Soto | D | 100.00 | 100 | 100 | 100 |
| David Cicilline | D | 100.00 | 100 | 100 | 100 |
| David Joyce | R | 17.00 | 26 | 7 | 18 |
| David Kustoff | R | 0.00 | 0 | 0 | 0 |
| David McKinley | R | 2.00 | 6 | 0 | 0 |
| David Price | D | 100.00 | 100 | 100 | 100 |
| David Rouzer | R | 0.00 | 0 | 0 | 0 |
| David Schweikert | R | 0.00 | 0 | 0 | 0 |
| David Scott | D | 96.00 | 100 | 100 | 88 |
| David Trone | D | 100.00 | 100 | 100 | NA |
| David Valadao | R | 27.00 | 34 | NA | 20 |
| Dean Phillips | D | 100.00 | 100 | 100 | NA |
| Debbie Dingell | D | 100.00 | 100 | 100 | 100 |
| Debbie Lesko | R | 0.00 | 0 | 0 | 0 |
| Debbie Wasserman Schultz | D | 100.00 | 100 | 100 | 100 |
| Deborah Ross | D | 100.00 | 100 | NA | NA |
| Derek Kilmer | D | 100.00 | 100 | 100 | 100 |
| Diana DeGette | D | 100.00 | 100 | 100 | 100 |
| Diana Harshbarger | R | 0.00 | 0 | NA | NA |
| Dina Titus | D | 100.00 | 100 | 100 | 100 |
| Don Bacon | R | 9.00 | 20 | 7 | 0 |
| Don Beyer | D | 100.00 | 100 | 100 | 100 |
| Donald McEachin | D | 100.00 | 100 | 100 | 100 |
| Donald Norcross | D | 100.00 | 100 | 100 | 100 |
| Donald Payne Jr, | D | 100.00 | 100 | 100 | 100 |
| Doris Matsui | D | 100.00 | 100 | 100 | 100 |
| Doug LaMalfa | R | 0.00 | 0 | 0 | 0 |
| Doug Lamborn | R | 0.00 | 0 | 0 | 0 |
| Drew Ferguson | R | 0.00 | 0 | 0 | 0 |
| Dusty Johnson | R | 0.00 | 0 | 0 | NA |
| Dutch Ruppersberger | D | 100.00 | 100 | 100 | 100 |
| Dwight Evans | D | 100.00 | 100 | 100 | 100 |
| Earl Blumenauer | D | 100.00 | 100 | 100 | 100 |
| Ed Case | D | 96.50 | 96 | 97 | NA |
| Ed Perlmutter | D | 99.33 | 98 | 100 | 100 |
| Eddie Bernice Johnson | D | 96.00 | 100 | 100 | 88 |
| Elaine Luria | D | 95.50 | 100 | 91 | NA |
| Eleanor Holmes Norton | D | 100.00 | 100 | 100 | 100 |
| Elise Stefanik | R | 31.33 | 8 | 40 | 46 |
| Elissa Slotkin | D | 100.00 | 100 | 100 | NA |
| Emanuel Cleaver | D | 90.33 | 98 | 97 | 76 |
| Eric Swalwell | D | 100.00 | 100 | 100 | 100 |
| Frank Lucas | R | 0.00 | 0 | 0 | 0 |
| Frank Mrvan | D | 98.00 | 98 | NA | NA |
| Frank Pallone Jr, | D | 98.67 | 96 | 100 | 100 |
| Fred Keller | R | 0.00 | 0 | 0 | NA |
| Fred Upton | R | 38.33 | 58 | 42 | 15 |
| Frederica Wilson | D | 97.00 | 100 | 100 | 91 |
| French Hill | R | 0.00 | 0 | 0 | 0 |
| Garret Graves | R | 0.00 | 0 | 0 | 0 |
| Gary Palmer | R | 0.00 | 0 | 0 | 0 |
| Gerald Connolly | D | 100.00 | 100 | 100 | 100 |
| GK. Butterfield, | D | 87.33 | 92 | 88 | 82 |
| Glenn Grothman | R | 0.00 | 0 | 0 | 0 |
| Glenn Thompson | R | 0.00 | 0 | 0 | 0 |
| Grace Meng | D | 100.00 | 100 | 100 | 100 |
| Grace Napolitano | D | 100.00 | 100 | 100 | 100 |
| Greg Murphy | R | 6.00 | 6 | NA | NA |
| Greg Pence | R | 3.50 | 7 | 0 | NA |
| Greg Stanton | D | 100.00 | 100 | 100 | NA |
| Greg Steube | R | 0.00 | 0 | 0 | NA |
| Gregorio Sablan | D | 63.00 | 80 | 69 | 40 |
| Gregory Meeks | D | 98.33 | 98 | 100 | 97 |
| Gus Bilirakis | R | 0.00 | 0 | 0 | 0 |
| Guy Reschenthaler | R | 0.00 | 0 | 0 | NA |
| Gwen Moore | D | 96.33 | 98 | 97 | 94 |
| Hakeem Jeffries | D | 100.00 | 100 | 100 | 100 |
| Haley Stevens | D | 100.00 | 100 | 100 | NA |
| Harold Rogers | R | 0.67 | 2 | 0 | 0 |
| Henry Cuellar | D | 81.33 | 84 | 75 | 85 |
| Henry Johnson Jr, | D | 100.00 | 100 | 100 | 100 |
| Ilhan Omar | D | 100.00 | 100 | 100 | NA |
| Jack Bergman | R | 8.33 | 0 | 0 | 25 |
| Jackie Speier | D | 99.33 | 98 | 100 | 100 |
| Jahana Hayes | D | 100.00 | 100 | 100 | NA |
| Jaime Herrera Beutler | R | 11.00 | 10 | 8 | 15 |
| Jake Auchincloss | D | 100.00 | 100 | NA | NA |
| Jake Ellzey | R | 0.00 | 0 | 0 | NA |
| Jake LaTurner | R | 0.00 | 0 | NA | NA |
| Jamaal Bowman | D | 100.00 | 100 | NA | NA |
| James Clyburn | D | 95.00 | 100 | 100 | 85 |
| James Comer | R | 0.00 | 0 | 0 | 0 |
| James McGovern | D | 100.00 | 100 | 100 | 100 |
| Jamie Raskin | D | 100.00 | 100 | 100 | 100 |
| Jan Schakowsky | D | 100.00 | 100 | 100 | 100 |
| Jared Golden | D | 90.00 | 90 | 90 | NA |
| Jared Huffman | D | 100.00 | 100 | 100 | 100 |
| Jason Crow | D | 100.00 | 100 | 100 | NA |
| Jason Smith | R | 0.00 | 0 | 0 | 0 |
| Jay Obernolte | R | 14.00 | 14 | NA | NA |
| Jeff Duncan | R | 0.00 | 0 | 0 | 0 |
| Jeff Van Drew | R | 50.00 | 22 | 78 | NA |
| Jennifer Wexton | D | 100.00 | 100 | 100 | NA |
| Jenniffer GonzálezColón, | R | 9.67 | 20 | 9 | 0 |
| Jerrold Nadler | D | 100.00 | 100 | 100 | 100 |
| Jerry Carl | R | 0.00 | 0 | NA | NA |
| Jerry McNerney | D | 98.33 | 98 | 100 | 97 |
| Jesús García | D | 100.00 | 100 | 100 | NA |
| Jim Baird | R | 0.00 | 0 | 0 | NA |
| Jim Banks | R | 0.00 | 0 | 0 | 0 |
| Jim Cooper | D | 97.33 | 98 | 100 | 94 |
| Jim Costa | D | 98.00 | 100 | 97 | 97 |
| Jim Himes | D | 100.00 | 100 | 100 | 100 |
| Jim Jordan | R | 0.00 | 0 | 0 | 0 |
| Jim Langevin | D | 100.00 | 100 | 100 | 100 |
| Jimmy Gomez | D | 100.00 | 100 | 100 | 100 |
| Jimmy Panetta | D | 100.00 | 100 | 100 | 100 |
| Joaquin Castro | D | 100.00 | 100 | 100 | 100 |
| Jodey Arrington | R | 0.00 | 0 | 0 | 0 |
| Jody Hice | R | 0.00 | 0 | 0 | 0 |
| Joe Courtney | D | 100.00 | 100 | 100 | 100 |
| Joe Neguse | D | 100.00 | 100 | 100 | NA |
| Joe Sempolinski | R | NaN | NA | NA | NA |
| Joe Wilson | R | 0.00 | 0 | 0 | 0 |
| John Carter | R | 4.33 | 6 | 7 | 0 |
| John Curtis | R | 2.67 | 8 | 0 | 0 |
| John Garamendi | D | 100.00 | 100 | 100 | 100 |
| John Joyce | R | 1.00 | 2 | 0 | NA |
| John Katko | R | 61.00 | 68 | 54 | 61 |
| John Larson | D | 100.00 | 100 | 100 | 100 |
| John Moolenaar | R | 0.00 | 0 | 0 | 0 |
| John Rose | R | 0.00 | 0 | 0 | NA |
| John Rutherford | R | 0.00 | 0 | 0 | 0 |
| John Sarbanes | D | 97.00 | 100 | 100 | 91 |
| John Yarmuth | D | 100.00 | 100 | 100 | 100 |
| Joseph Morelle | D | 100.00 | 100 | 100 | NA |
| Josh Gottheimer | D | 100.00 | 100 | 100 | 100 |
| Josh Harder | D | 97.00 | 94 | 100 | NA |
| Joyce Beatty | D | 100.00 | 100 | 100 | 100 |
| Juan Vargas | D | 100.00 | 100 | 100 | 100 |
| Judy Chu | D | 100.00 | 100 | 100 | 100 |
| Julia Brownley | D | 100.00 | 100 | 100 | 100 |
| Julia Letlow | R | 0.00 | 0 | NA | NA |
| Kaialii Kahele, | D | 94.00 | 94 | NA | NA |
| Karen Bass | D | 100.00 | 100 | 100 | 100 |
| Kat Cammack | R | 8.00 | 8 | NA | NA |
| Katherine Clark | D | 100.00 | 100 | 100 | 100 |
| Kathleen Rice | D | 100.00 | 100 | 100 | 100 |
| Kathy Castor | D | 98.00 | 94 | 100 | 100 |
| Kathy Manning | D | 100.00 | 100 | NA | NA |
| Katie Porter | D | 100.00 | 100 | 100 | NA |
| Kay Granger | R | 0.67 | 2 | 0 | 0 |
| Kelly Armstrong | R | 4.00 | 8 | 0 | NA |
| Ken Buck | R | 0.00 | 0 | 0 | 0 |
| Ken Calvert | R | 4.67 | 14 | 0 | 0 |
| Kevin Brady | R | 0.67 | 2 | 0 | 0 |
| Kevin Hern | R | 0.00 | 0 | 0 | NA |
| Kevin McCarthy | R | 0.00 | 0 | 0 | 0 |
| Kim Schrier | D | 100.00 | 100 | 100 | NA |
| Kurt Schrader | D | 99.00 | 100 | 97 | 100 |
| Kweisi Mfume | D | 100.00 | 100 | NA | NA |
| Lance Gooden | R | 0.00 | 0 | 0 | NA |
| Larry Bucshon | R | 0.00 | 0 | 0 | 0 |
| Lauren Boebert | R | 0.00 | 0 | NA | NA |
| Lauren Underwood | D | 98.50 | 100 | 97 | NA |
| Lee Zeldin | R | 7.33 | 12 | 7 | 3 |
| Linda Sánchez | D | 100.00 | 100 | 100 | 100 |
| Lisa Blunt Rochester | D | 98.00 | 100 | 100 | 94 |
| Lisa McClain | R | 0.00 | 0 | NA | NA |
| Liz Cheney | R | 7.67 | 23 | 0 | 0 |
| Lizzie Fletcher | D | 100.00 | 100 | 100 | NA |
| Lloyd Doggett | D | 100.00 | 100 | 100 | 100 |
| Lloyd Smucker | R | 0.00 | 0 | 0 | 0 |
| Lois Frankel | D | 100.00 | 100 | 100 | 100 |
| Lori Trahan | D | 100.00 | 100 | 100 | NA |
| Lou Correa | D | 100.00 | 100 | 100 | 100 |
| Louie Gohmert | R | 0.00 | 0 | 0 | 0 |
| Lucille RoybalAllard, | D | 100.00 | 100 | 100 | 100 |
| Lucy McBath | D | 100.00 | 100 | 100 | NA |
| Madeleine Dean | D | 100.00 | 100 | 100 | NA |
| Madison Cawthorn | R | 0.00 | 0 | NA | NA |
| Marc Veasey | D | 97.00 | 94 | 100 | 97 |
| Marcy Kaptur | D | 99.00 | 100 | 97 | 100 |
| Maria Salazar | R | 38.00 | 38 | NA | NA |
| Mariannette MillerMeeks, | R | 20.00 | 20 | NA | NA |
| Marie Newman | D | 100.00 | 100 | NA | NA |
| Marilyn Strickland | D | 100.00 | 100 | NA | NA |
| Mario DiazBalart, | R | 25.67 | 22 | 40 | 15 |
| Marjorie Greene | R | 0.00 | 0 | NA | NA |
| Mark Amodei | R | 6.00 | 0 | 0 | 18 |
| Mark DeSaulnier | D | 100.00 | 100 | 100 | 100 |
| Mark Green | R | 0.00 | 0 | 0 | NA |
| Mark Pocan | D | 100.00 | 100 | 100 | 100 |
| Mark Takano | D | 100.00 | 100 | 100 | 100 |
| Markwayne Mullin | R | 2.00 | 6 | 0 | 0 |
| Mary Miller | R | 0.00 | 0 | NA | NA |
| Mary Peltola | D | NaN | NA | NA | NA |
| Mary Scanlon | D | 100.00 | 100 | 100 | NA |
| Matt Gaetz | R | 0.00 | 0 | 0 | 0 |
| Matthew Cartwright | D | 100.00 | 100 | 100 | 100 |
| Matthew Rosendale | R | 0.00 | 0 | NA | NA |
| Maxine Waters | D | 100.00 | 100 | 100 | 100 |
| Mayra Flores | R | NaN | NA | NA | NA |
| Melanie Stansbury | D | 100.00 | 100 | NA | NA |
| Michael Burgess | R | 0.00 | 0 | 0 | 0 |
| Michael Cloud | R | 0.00 | 0 | 0 | NA |
| Michael Guest | R | 0.00 | 0 | 0 | NA |
| Michael McCaul | R | 5.00 | 8 | 7 | 0 |
| Michael San Nicolas | D | 64.50 | 60 | 69 | NA |
| Michael Turner | R | 13.00 | 15 | 9 | 15 |
| Michael Waltz | R | 7.50 | 8 | 7 | NA |
| Michelle Fischbach | R | 0.00 | 0 | NA | NA |
| Michelle Steel | R | 6.00 | 6 | NA | NA |
| Mike Bost | R | 8.67 | 18 | 8 | 0 |
| Mike Carey | R | NaN | NA | NA | NA |
| Mike Doyle | D | 100.00 | 100 | 100 | 100 |
| Mike Flood | R | NaN | NA | NA | NA |
| Mike Gallagher | R | 1.33 | 4 | 0 | 0 |
| Mike Garcia | R | 14.00 | 14 | NA | NA |
| Mike Johnson | R | 0.00 | 0 | 0 | 0 |
| Mike Kelly | R | 0.00 | 0 | 0 | 0 |
| Mike Levin | D | 100.00 | 100 | 100 | NA |
| Mike Quigley | D | 100.00 | 100 | 100 | 100 |
| Mike Rogers | R | 0.00 | 0 | 0 | 0 |
| Mike Simpson | R | 7.67 | 16 | 7 | 0 |
| Mike Thompson | D | 100.00 | 100 | 100 | 100 |
| Mikie Sherrill | D | 100.00 | 100 | 100 | NA |
| Mo Brooks | R | 0.00 | 0 | 0 | 0 |
| Mondaire Jones | D | 100.00 | 100 | NA | NA |
| Morgan Griffith | R | 2.00 | 6 | 0 | 0 |
| Nancy Mace | R | 14.00 | 14 | NA | NA |
| Nancy Pelosi | D | 100.00 | 100 | 100 | 100 |
| Nanette Barragán | D | 100.00 | 100 | 100 | 100 |
| Neal Dunn | R | 0.00 | 0 | 0 | 0 |
| Nicole Malliotakis | R | 20.00 | 20 | NA | NA |
| Nikema Williams | D | 100.00 | 100 | NA | NA |
| Norma Torres | D | 100.00 | 100 | 100 | 100 |
| Nydia Velázquez | D | 100.00 | 100 | 100 | 100 |
| Pat Fallon | R | 0.00 | 0 | 0 | NA |
| Pat Ryan | D | NaN | NA | NA | NA |
| Patrick McHenry | R | 2.67 | 8 | 0 | 0 |
| Paul Gosar | R | 0.00 | 0 | 0 | 0 |
| Paul Tonko | D | 100.00 | 100 | 100 | 100 |
| Pete Aguilar | D | 100.00 | 100 | 100 | 100 |
| Pete Sessions | R | 0.00 | 0 | NA | 0 |
| Pete Stauber | R | 6.50 | 6 | 7 | NA |
| Peter DeFazio | D | 100.00 | 100 | 100 | 100 |
| Peter Meijer | R | 40.00 | 40 | NA | NA |
| Peter Welch | D | 100.00 | 100 | 100 | 100 |
| Pramila Jayapal | D | 100.00 | 100 | 100 | 100 |
| Raja Krishnamoorthi | D | 100.00 | 100 | 100 | 100 |
| Ralph Norman | R | 0.00 | 0 | 0 | 0 |
| Randy Feenstra | R | 0.00 | 0 | NA | NA |
| Randy Weber | R | 0.00 | 0 | 0 | 0 |
| Rashida Tlaib | D | 100.00 | 100 | 100 | NA |
| Raúl Grijalva | D | 100.00 | 100 | 100 | 100 |
| Raul Ruiz | D | 100.00 | 100 | 100 | 100 |
| Richard Hudson | R | 0.00 | 0 | 0 | 0 |
| Richard Neal | D | 99.33 | 98 | 100 | 100 |
| Rick Allen | R | 0.00 | 0 | 0 | 0 |
| Rick Crawford | R | 0.00 | 0 | 0 | 0 |
| Rick Larsen | D | 100.00 | 100 | 100 | 100 |
| Ritchie Torres | D | 100.00 | 100 | NA | NA |
| Ro Khanna | D | 99.33 | 98 | 100 | 100 |
| Robert Aderholt | R | 0.00 | 0 | 0 | 0 |
| Robert Latta | R | 0.00 | 0 | 0 | 0 |
| Robert Scott | D | 96.00 | 94 | 97 | 97 |
| Robert Wittman | R | 0.00 | 0 | 0 | 0 |
| Robin Kelly | D | 100.00 | 100 | 100 | 100 |
| Rodney Davis | R | 11.00 | 26 | 7 | 0 |
| Roger Williams | R | 2.33 | 0 | 7 | 0 |
| Ron Estes | R | 0.00 | 0 | 0 | 0 |
| Ron Kind | D | 99.33 | 98 | 100 | 100 |
| Ronny Jackson | R | 0.00 | 0 | NA | NA |
| Rosa DeLauro | D | 99.33 | 98 | 100 | 100 |
| Ruben Gallego | D | 100.00 | 100 | 100 | 100 |
| Rudy Yakym | R | NaN | NA | NA | NA |
| Russ Fulcher | R | 0.00 | 0 | 0 | NA |
| Salud Carbajal | D | 100.00 | 100 | 100 | 100 |
| Sam Graves | R | 0.00 | 0 | 0 | 0 |
| Sanford Bishop Jr, | D | 89.00 | 98 | 87 | 82 |
| Sara Jacobs | D | 100.00 | 100 | NA | NA |
| Scott DesJarlais | R | 0.00 | 0 | 0 | 0 |
| Scott Fitzgerald | R | 0.00 | 0 | NA | NA |
| Scott Perry | R | 2.00 | 6 | 0 | 0 |
| Scott Peters | D | 100.00 | 100 | 100 | 100 |
| Sean Casten | D | 100.00 | 100 | 100 | NA |
| Sean Maloney | D | 100.00 | 100 | 100 | 100 |
| Seth Moulton | D | 100.00 | 100 | 100 | 100 |
| Sharice Davids | D | 98.50 | 100 | 97 | NA |
| Sheila CherfilusMcCormick, | D | NaN | NA | NA | NA |
| Sheila Jackson Lee | D | 99.33 | 98 | 100 | 100 |
| Shontel Brown | D | NaN | NA | NA | NA |
| Stacey Plaskett | D | 41.00 | 20 | 53 | 50 |
| Steny Hoyer | D | 99.00 | 100 | 100 | 97 |
| Stephanie Bice | R | 6.00 | 6 | NA | NA |
| Stephanie Murphy | D | 98.00 | 94 | 100 | 100 |
| Stephen Lynch | D | 100.00 | 100 | 100 | 100 |
| Steve Chabot | R | 2.00 | 6 | 0 | 0 |
| Steve Cohen | D | 100.00 | 100 | 100 | 100 |
| Steve Scalise | R | 0.00 | 0 | 0 | 0 |
| Steve Womack | R | 0.00 | 0 | 0 | 0 |
| Steven Horsford | D | 100.00 | 100 | 100 | NA |
| Steven Palazzo | R | 0.00 | 0 | 0 | 0 |
| Susan Wild | D | 100.00 | 100 | 100 | NA |
| Susie Lee | D | 97.50 | 98 | 97 | NA |
| Suzan DelBene | D | 100.00 | 100 | 100 | 100 |
| Suzanne Bonamici | D | 100.00 | 100 | 100 | 100 |
| Sylvia Garcia | D | 100.00 | 100 | 100 | NA |
| Ted Budd | R | 0.00 | 0 | 0 | 0 |
| Ted Lieu | D | 100.00 | 100 | 100 | 100 |
| Teresa Leger Fernández | D | 100.00 | 100 | NA | NA |
| Terri Sewell | D | 92.67 | 96 | 97 | 85 |
| Thomas Massie | R | 5.00 | 0 | 0 | 15 |
| Thomas Suozzi | D | 100.00 | 100 | 100 | 100 |
| Tim Burchett | R | 4.50 | 0 | 9 | NA |
| Tim Ryan | D | 100.00 | 100 | 100 | 100 |
| Tim Walberg | R | 0.00 | 0 | 0 | 0 |
| Tom Cole | R | 8.33 | 18 | 7 | 0 |
| Tom Emmer | R | 4.67 | 14 | 0 | 0 |
| Tom Malinowski | D | 100.00 | 100 | 100 | NA |
| Tom McClintock | R | 0.00 | 0 | 0 | 0 |
| Tom OHalleran, | D | 99.00 | 100 | 97 | 100 |
| Tom Rice | R | 6.67 | 20 | 0 | 0 |
| Tom Tiffany | R | 0.00 | 0 | NA | NA |
| Tony Cárdenas | D | 99.33 | 98 | 100 | 100 |
| Tony Gonzales | R | 20.00 | 20 | NA | NA |
| Tracey Mann | R | 0.00 | 0 | NA | NA |
| Trent Kelly | R | 0.00 | 0 | 0 | 0 |
| Trey Hollingsworth | R | 5.33 | 2 | 14 | 0 |
| Troy Balderson | R | 6.50 | 6 | 7 | NA |
| Troy Carter | D | 88.00 | 88 | NA | NA |
| Troy Nehls | R | 0.00 | 0 | NA | NA |
| Val Demings | D | 100.00 | 100 | 100 | 100 |
| Van Taylor | R | 0.00 | 0 | 0 | NA |
| Vern Buchanan | R | 4.33 | 6 | 7 | 0 |
| Veronica Escobar | D | 100.00 | 100 | 100 | NA |
| Vicente Gonzalez | D | 91.00 | 100 | 88 | 85 |
| Vicky Hartzler | R | 0.00 | 0 | 0 | 0 |
| Victoria Spartz | R | 0.00 | 0 | NA | NA |
| Virginia Foxx | R | 0.00 | 0 | 0 | 0 |
| Warren Davidson | R | 0.00 | 0 | 0 | 0 |
| William Timmons | R | 0.00 | 0 | 0 | NA |
| Young Kim | R | 26.00 | 26 | NA | NA |
| Yvette Clarke | D | 100.00 | 100 | 100 | 100 |
| Yvette Herrell | R | 0.00 | 0 | NA | NA |
| Zoe Lofgren | D | 100.00 | 100 | 100 | 100 |
Senators
#rename score variabels
hrc_sen <- hrc_sen %>%
rename(score_117 = score1,
score_116 = score2,
score_115 = score3)
#CHANGE THE CHARACTERS RE VOTING
hrc_sen %>% mutate_at(vars(5:24),
funs(case_when(
. == "j" ~ "anti-HRC",
. =="v" ~ "pro-HRC",
. == "J" ~ "did not vote",
. == "P" ~ "voted present",
TRUE ~ .
))) -> hrc_sen
#clean up names
hrc_sen %>% separate(name, into = c("Name", "ID"),
sep = "\\(") %>%
separate(Name, into = c("spot1", "spot2", "spot3", "spot4", "spot5"),
sep = "[[:blank:]]") %>%
filter(str_detect(spot1, "\\,")) %>%
select(-spot3, -spot4, -spot5)%>%
rename(last_name = spot1, first_name = spot2) -> hrc_sen_df
hrc_sen %>% separate(name, into = c("Name", "ID"),
sep = "\\(") %>%
separate(Name, into = c("spot1", "spot2", "spot3", "spot4", "spot5"),
sep = "[[:blank:]]") %>%
filter(str_detect(spot2, "\\,")) %>%
unite("last_name", spot1, spot2, sep = " ") %>%
select(-spot4, -spot5) %>%
rename(first_name = spot3) -> hrc_sen_df2
hrc_sen_df <- bind_rows(hrc_sen_df, hrc_sen_df2)
hrc_sen %>% separate(name, into = c("Name", "ID"),
sep = "\\(") %>%
separate(Name, into = c("spot1", "spot2", "spot3", "spot4", "spot5"),
sep = "[[:blank:]]") %>%
filter(str_detect(spot3, "\\,")) %>%
select(-spot1, -spot2, -spot5) %>%
rename(last_name = spot3, first_name = spot4) -> hrc_sen_df2
hrc_sen_df <- bind_rows(hrc_sen_df, hrc_sen_df2)
hrc_sen %>% separate(name, into = c("Name", "ID"),
sep = "\\(") %>%
separate(Name, into = c("spot1", "spot2", "spot3", "spot4", "spot5"),
sep = "[[:blank:]]") %>%
filter(str_detect(spot4, "\\,")) %>%
select(-spot1, -spot2, -spot3) %>%
rename(last_name = spot4, first_name = spot5) -> hrc_sen_df2
hrc_sen_df <- bind_rows(hrc_sen_df, hrc_sen_df2)
#make a new column with their name
hrc_sen_df <- hrc_sen_df %>% unite("name", first_name, last_name, sep = " ", remove = F)
hrc_sen_df <- hrc_sen_df %>% mutate(name = str_replace_all(name,"[[:punct:]]", ""))
hrc_sen_df$name <- str_squish(hrc_sen_df$name)
#Robert/Bob Casey's name got messed up
hrc_sen_df <- hrc_sen_df %>%
mutate(name = case_when(
name == "Robert Jr" ~ "Bob Casey",
TRUE ~ name),
last_name = case_when(
last_name == "Jr.," ~ "Casey Jr.",
TRUE ~ last_name
))
# Convert scores to numeric
hrc_sen_df$score_117 <- as.numeric(hrc_sen_df$score_117)
hrc_sen_df$score_116 <- as.numeric(hrc_sen_df$score_116)
hrc_sen_df$score_115 <- as.numeric(hrc_sen_df$score_115)#Calculate average score based on previous voting record
hrc_sen_df %>%
select(-last_name, -first_name) %>% #drop columns for tidiness
pivot_longer(score_117:score_115, names_to = "session", values_to = "score") %>%
group_by(name) %>% #group reps
summarise(name = name,
avg_score = round(mean(score, na.rm = T),2)) %>%
unique() %>% #drop duplicates
ungroup() %>% #ungroup observations
right_join(hrc_sen_df, by = "name") -> hrc_sen_dfThe table below shows the Senators and their scores of equality based on their voting record (when available). The column avg_score calculate the average of the available equality scores from the HRC. Not all Senators served in prior congressional sessions, which is reflected by “NA” and does not count towards the avg_score.
#print table
hrc_sen_df %>%
select(name, ID, avg_score, score_117, score_116, score_115) %>%
arrange(name)%>%
kbl(table.attr = "style = \"color: black;\"") %>%
kable_styling(bootstrap_options = c("striped", "hover"), position = "center") %>%
scroll_box(height = "400px")| name | ID | avg_score | score_117 | score_116 | score_115 |
|---|---|---|---|---|---|
| Alex Padilla | D | 100.00 | 100 | NA | NA |
| Amy Klobuchar | D | 100.00 | 100 | 100 | 100 |
| Angus King | I | 89.00 | 92 | 87 | 88 |
| Ben Luján | D | 100.00 | 100 | NA | NA |
| Ben Sasse | R | 4.33 | 13 | 0 | 0 |
| Benjamin Cardin | D | 100.00 | 100 | 100 | 100 |
| Bernard Sanders | I | 100.00 | 100 | 100 | 100 |
| Bill Hagerty | R | 0.00 | 0 | NA | NA |
| Bob Casey | D | 97.33 | 98 | 97 | 97 |
| Brian Schatz | D | 90.33 | 92 | 88 | 91 |
| Catherine Cortez Masto | D | 99.00 | 100 | 97 | 100 |
| Charles Schumer | D | 100.00 | 100 | 100 | 100 |
| Chris Coons | D | 97.00 | 100 | 97 | 94 |
| Chris Van Hollen | D | 100.00 | 100 | 100 | 100 |
| Christopher Murphy | D | 100.00 | 100 | 100 | 100 |
| Chuck Grassley | R | 1.67 | 5 | 0 | 0 |
| Cindy HydeSmith | R | 2.50 | 5 | 0 | NA |
| Cory Booker | D | 100.00 | 100 | 100 | 100 |
| Cynthia Lummis | R | 13.00 | 13 | NA | NA |
| Dan Sullivan | R | 4.67 | 14 | 0 | 0 |
| Deb Fischer | R | 1.67 | 5 | 0 | 0 |
| Debbie Stabenow | D | 100.00 | 100 | 100 | 100 |
| Dianne Feinstein | D | 100.00 | 100 | 100 | 100 |
| Ed Markey | D | 100.00 | 100 | 100 | 100 |
| Elizabeth Warren | D | 100.00 | 100 | 100 | 100 |
| Gary Peters | D | 93.67 | 96 | 91 | 94 |
| Jack Reed | D | 100.00 | 100 | 100 | 100 |
| Jacky Rosen | D | 100.00 | 100 | 100 | NA |
| James Inhofe | R | 1.67 | 5 | 0 | 0 |
| James Lankford | R | 0.00 | 0 | 0 | 0 |
| James Risch | R | 1.67 | 5 | 0 | 0 |
| Jeanne Shaheen | D | 100.00 | 100 | 100 | 100 |
| Jeff Merkley | D | 100.00 | 100 | 100 | 100 |
| Jerry Moran | R | 1.67 | 5 | 0 | 0 |
| John Barrasso | R | 1.67 | 5 | 0 | 0 |
| John Boozman | R | 1.67 | 5 | 0 | 0 |
| John Cornyn | R | 3.67 | 11 | 0 | 0 |
| John Hickenlooper | D | 100.00 | 100 | NA | NA |
| John Hoeven | R | 1.67 | 5 | 0 | 0 |
| John Kennedy | R | 3.33 | 10 | 0 | 0 |
| John Thune | R | 1.67 | 5 | 0 | 0 |
| Jon Ossoff | D | 92.00 | 92 | NA | NA |
| Jon Tester | D | 86.67 | 90 | 82 | 88 |
| Joni Ernst | R | 6.33 | 19 | 0 | 0 |
| Joseph Manchin | D | 45.67 | 49 | 58 | 30 |
| Josh Hawley | R | 0.00 | 0 | 0 | NA |
| Kevin Cramer | R | 2.50 | 5 | 0 | NA |
| Kirsten Gillibrand | D | 98.67 | 96 | 100 | 100 |
| Kyrsten Sinema | D | 88.00 | 90 | 86 | NA |
| Lindsey Graham | R | 9.33 | 16 | 0 | 12 |
| Lisa Murkowski | R | 37.33 | 55 | 3 | 54 |
| Maggie Hassan | D | 100.00 | 100 | 100 | 100 |
| Marco Rubio | R | 0.00 | 0 | 0 | 0 |
| Maria Cantwell | D | 98.33 | 98 | 97 | 100 |
| Mark Kelly | D | 94.00 | 94 | NA | NA |
| Mark Warner | D | 92.00 | 94 | 91 | 91 |
| Marsha Blackburn | R | 0.00 | 0 | 0 | NA |
| Martin Heinrich | D | 96.00 | 100 | 97 | 91 |
| Mazie Hirono | D | 99.33 | 98 | 100 | 100 |
| Michael Bennet | D | 92.67 | 94 | 93 | 91 |
| Mike Braun | R | 2.50 | 5 | 0 | NA |
| Mike Crapo | R | 1.67 | 5 | 0 | 0 |
| Mike Lee | R | 1.67 | 5 | 0 | 0 |
| Mike Rounds | R | 2.00 | 6 | 0 | 0 |
| Mitch McConnell | R | 4.67 | 14 | 0 | 0 |
| Mitt Romney | R | 19.00 | 31 | 7 | NA |
| Patrick Leahy | D | 98.00 | 94 | 100 | 100 |
| Patrick Toomey | R | 5.00 | 15 | 0 | 0 |
| Patty Murray | D | 99.33 | 98 | 100 | 100 |
| Rand Paul | R | 4.33 | 5 | 8 | 0 |
| Raphael Warnock | D | 86.00 | 86 | NA | NA |
| Richard Blumenthal | D | 100.00 | 100 | 100 | 100 |
| Richard Burr | R | 8.33 | 25 | 0 | 0 |
| Richard Durbin | D | 100.00 | 100 | 100 | 100 |
| Richard Shelby | R | 0.00 | 0 | 0 | 0 |
| Rick Scott | R | 0.00 | 0 | 0 | NA |
| Rob Portman | R | 6.33 | 19 | 0 | 0 |
| Robert Menendez | D | 99.33 | 98 | 100 | 100 |
| Roger Marshall | R | 0.00 | 0 | NA | NA |
| Roger Wicker | R | 1.67 | 5 | 0 | 0 |
| Ron Johnson | R | 1.67 | 5 | 0 | 0 |
| Ron Wyden | D | 99.33 | 98 | 100 | 100 |
| Roy Blunt | R | 6.33 | 19 | 0 | 0 |
| Sheldon Whitehouse | D | 100.00 | 100 | 100 | 100 |
| Shelley Capito | R | 6.33 | 19 | 0 | 0 |
| Sherrod Brown | D | 100.00 | 100 | 100 | 100 |
| Steven Daines | R | 1.67 | 5 | 0 | 0 |
| Susan Collins | R | 46.00 | 54 | 51 | 33 |
| Tammy Baldwin | D | 100.00 | 100 | 100 | 100 |
| Tammy Duckworth | D | 100.00 | 100 | 100 | 100 |
| Ted Cruz | R | 0.00 | 0 | 0 | 0 |
| Thom Tillis | R | 6.67 | 20 | 0 | 0 |
| Thomas Carper | D | 100.00 | 100 | 100 | 100 |
| Tim Kaine | D | 93.67 | 96 | 94 | 91 |
| Tim Scott | R | 0.00 | 0 | 0 | 0 |
| Tina Smith | D | 100.00 | 100 | 100 | 100 |
| Todd Young | R | 6.33 | 19 | 0 | 0 |
| Tom Cotton | R | 0.00 | 0 | 0 | 0 |
| Tommy Tuberville | R | 0.00 | 0 | 0 | NA |
| William Cassidy | R | 4.00 | 12 | 0 | 0 |
ASHA-PAC’s Spending by HRC Score
#Make sure names align across DFs
### Representatives
hrc_rep_df$name <- str_squish(hrc_rep_df$name)
hrc_rep_df %>% mutate(name = case_when(
name == "Frank Pallone Jr," ~ "Frank Pallone Jr.",
name == "Joe Neguse" ~ "Joseph Neguse",
name == "GK. Butterfield," ~ "G K Butterfield",
name == "Lauren Underwood" ~ "Lauren A Underwood",
name == "Robert Latta" ~ "Bob Latta",
name == "Robert Scott" ~ "Bobby Scott",
name == "Richard Neal" ~ "Richard E Neal",
name == "Bobby Rush" ~ "Bobby L Rush",
name == "Steny Hoyer" ~ "Steny H Hoyer",
name == "Vern Buchanan" ~ "Vernon Buchanan",
name == "Matthew Cartwright" ~ "Matt Cartwright",
name == "Yvette Clarke" ~ "Yvette D Clarke",
name == "James Clyburn" ~ "James E Clyburn",
name == "Danny Davis" ~ "Danny K Davis",
name == "Tom OHalleran," ~ "Tom O'Halleran",
name == "Bill Pascrell Jr," ~ "Bill Pascrell Jr.",
name == "Lucille RoybalAllard," ~ "Lucille Roybal-Allard",
name == "Terri Sewell" ~ "Terri A Sewell",
name == "Thomas Suozzi" ~ "Tom Suozzi",
TRUE ~ name
)) -> hrc_rep_df
ashaPAC %>% mutate(name = case_when(
name == "Mark Desaulnier" ~ "Mark DeSaulnier",
name == "David P Joyce" ~ "David Joyce",
name == "Nanette Barragan" ~ "Nanette Barragán",
name == "Tony Cardenas" ~ "Tony Cárdenas",
name == "Yvette Clarke" ~ "Yvette D Clarke",
name == "Linda Sanchez" ~ "Linda Sánchez",
name == "Terri Sewell" ~ "Terri A Sewell",
TRUE ~ name
)) -> ashaPAC#The following names are Representatives not in the HRC data for the 117th congress. The ones who are not included were in the 116th congress. Their data will be manually added based on the [HRC's report on the 116th Congress](https://hrc-prod-requests.s3-us-west-2.amazonaws.com/116th-Congressional-Scorecard-060921.pdf).
# ashaPAC %>% filter(role == "Representative") %>%
# anti_join(hrc_rep_df, by = "name") %>%
# kbl(table.attr = "style = \"color: black;\"") %>%
# kable_styling(bootstrap_options = c("striped", "hover"), position = "center") %>%
# scroll_box(height = "400px")### Clean up the names of the Senators
hrc_sen_df$name <- str_squish(hrc_sen_df$name)
hrc_sen_df %>% mutate(name = case_when(
name == "Charles Schumer" ~ "Chuck Schumer",
name == "Cindy HydeSmith" ~ "Cindy Hyde-Smith",
name == "William Cassidy" ~ "Bill Cassidy",
name == "Richard Durbin" ~ "Dick Durbin",
name == "Shelley Capito" ~ "Shelley Moore Capito",
TRUE ~ name
)) -> hrc_sen_df
ashaPAC %>% mutate(name = case_when(
name == "Ben Ray Lujan" ~ "Ben Luján",
TRUE ~ name
)) -> ashaPAC
#Remove dollar sign from ASHA-PAC total
ashaPAC <- ashaPAC %>% mutate(ASHA_PAC_total = str_replace_all(ASHA_PAC_total, "\\$", ""))
ashaPAC$ASHA_PAC_total<-as.numeric(ashaPAC$ASHA_PAC_total)#Senators whose names don't match up:
# ashaPAC %>% filter(role == "Senator") %>%
# anti_join(hrc_sen_df, by = "name") %>%
# kbl(table.attr = "style = \"color: black;\"") %>%
# kable_styling(bootstrap_options = c("striped", "hover"), position = "center") %>%
# scroll_box(height = "400px")2022
Senators
Senators who won their race, their HRC scores, and the amount that ASHA-PAC has donated to their 2022 campaigns.
#Combine data from HRC and ASHA-PAC spending
ashaPAC %>% select(name, party, state, ASHA_PAC_total, year, role) %>%
filter(year == "2022", role == "Senator") %>%
left_join(hrc_sen_df %>%
select(name,avg_score, score_117:score_115), by = "name") -> asha_2022_sen
#add Markwayne Mullin's info from Representative HRC data
asha_2022_sen <- asha_2022_sen %>%
mutate(score_117 = case_when(
name == "Markwayne Mullin" ~ 6,
TRUE ~ score_117),
score_116 = case_when(
name == "Markwayne Mullin" ~ 0,
TRUE ~ score_116),
score_115 = case_when(
name == "Markwayne Mullin" ~ 0,
TRUE ~ score_115),
avg_score = case_when(
name == "Markwayne Mullin" ~ (6/3),
TRUE ~ avg_score
))
asha_2022_sen %>%
arrange(desc(ASHA_PAC_total)) %>%
relocate(name, ASHA_PAC_total, avg_score:score_115)%>%
kbl(table.attr = "style = \"color: black;\"") %>%
kable_styling(bootstrap_options = c("striped", "hover"), position = "center") %>%
scroll_box(height = "400px")| name | ASHA_PAC_total | avg_score | score_117 | score_116 | score_115 | party | state | year | role |
|---|---|---|---|---|---|---|---|---|---|
| Patty Murray | 6000 | 99.33 | 98 | 100 | 100 | D | WA | 2022 | Senator |
| Maggie Hassan | 5000 | 100.00 | 100 | 100 | 100 | D | NH | 2022 | Senator |
| Ron Wyden | 5000 | 99.33 | 98 | 100 | 100 | D | OR | 2022 | Senator |
| Lisa Murkowski | 4000 | 37.33 | 55 | 3 | 54 | R | AK | 2022 | Senator |
| Bob Casey | 2500 | 97.33 | 98 | 97 | 97 | D | PA | 2022 | Senator |
| Chuck Grassley | 2500 | 1.67 | 5 | 0 | 0 | R | IA | 2022 | Senator |
| Markwayne Mullin | 2500 | 2.00 | 6 | 0 | 0 | R | OK | 2022 | Senator |
| Chuck Schumer | 2500 | 100.00 | 100 | 100 | 100 | D | NY | 2022 | Senator |
| Tina Smith | 2500 | 100.00 | 100 | 100 | 100 | D | MN | 2022 | Senator |
| John Thune | 2500 | 1.67 | 5 | 0 | 0 | R | SD | 2022 | Senator |
| Susan Collins | 2000 | 46.00 | 54 | 51 | 33 | R | ME | 2022 | Senator |
| Todd Young | 2000 | 6.33 | 19 | 0 | 0 | R | IN | 2022 | Senator |
| John Barrasso | 1000 | 1.67 | 5 | 0 | 0 | R | WY | 2022 | Senator |
| Sherrod Brown | 1000 | 100.00 | 100 | 100 | 100 | D | OH | 2022 | Senator |
| Cindy Hyde-Smith | 1000 | 2.50 | 5 | 0 | NA | R | MS | 2022 | Senator |
| Debbie Stabenow | 1000 | 100.00 | 100 | 100 | 100 | D | MI | 2022 | Senator |
paste("The total amount of money ASHA-PAC donated to Senators' 2022 winning campaigns:", sum(asha_2022_sen$ASHA_PAC_total), "dollars.")## [1] "The total amount of money ASHA-PAC donated to Senators' 2022 winning campaigns: 43000 dollars."Money by Most Recent HRC Rating
Splitting the score_117 into 0, 1-25, 26-50, 51-75, and 75-100 and see how much money is spent.
asha_2022_sen <- asha_2022_sen %>%
mutate(groups_117 = case_when(
score_117 == 0 ~ "0",
score_117 <= 25 ~ "1-25",
score_117 <= 50 ~ "26-50",
score_117 <= 75 ~ "51-75",
score_117 <= 100 ~ "76-100"
),
groups_116 = case_when(
score_116 == 0 ~ "0",
score_116 <= 25 ~ "1-25",
score_116 <= 50 ~ "26-50",
score_116 <= 75 ~ "51-75",
score_116 <= 100 ~ "76-100"))
asha_2022_sen %>%
group_by(groups_117) %>%
summarise(n = n(), money_spent = sum(ASHA_PAC_total),
percent_spent = round((money_spent/43000)*100,2)) %>%
as_tibble() %>% rmarkdown::paged_table()ASHA-PAC donated:
$14,000 to campaigns given an HRC score of less than 25
$6,000 to campaigns given an HRC score between 51-75
$20,500 to campaigns given an HRC score above 75
Approximately a third of their donations went towards candidates with a score of less than 25.
Money by Average HRC Rating
Splitting the avg_score into 0, 1-25, 26-50, 51-75, and 75-100 and see how much money is spent.
asha_2022_sen <- asha_2022_sen %>%
mutate(groups_avg = case_when(
avg_score == 0 ~ "0",
avg_score <= 25 ~ "1-25",
avg_score <= 50 ~ "26-50",
avg_score <= 75 ~ "51-75",
avg_score <= 100 ~ "76-100"
))
asha_2022_sen %>%
group_by(groups_avg) %>%
summarise(n = n(), money_spent = sum(ASHA_PAC_total),
percent_spent = round((money_spent/43000)*100,2)) %>%
as_tibble() %>% rmarkdown::paged_table()Based on Senators’ average score over the the most recent congressional sessions
ASHA-PAC donated:
$11,500 to campaigns given an HRC score of less than 25
$6,000 to campaigns given an HRC score between 26-50
$5,000 to campaigns given an HRC score between 51-75
$20,500 to campaigns given an HRC score above 75
Approximately 41% of their donations went to campaigns with an average record of an HRC equality score \(\leq\) 50.
Representatives
Representatives who won their race, their HRC scores, and the amount that ASHA-PAC has donated to their 2022 campaigns.
ashaPAC %>% select(name, party, state, ASHA_PAC_total, year, role) %>%
filter(year == "2022", role == "Representative") %>%
left_join(hrc_rep_df %>%
select(name,avg_score, score_117:score_115), by = "name") -> asha_2022_rep
asha_2022_rep %>%
arrange(desc(ASHA_PAC_total)) %>%
relocate(name, ASHA_PAC_total, avg_score:score_115)%>%
kbl(table.attr = "style = \"color: black;\"") %>%
kable_styling(bootstrap_options = c("striped", "hover"), position = "center") %>%
scroll_box(height = "400px")| name | ASHA_PAC_total | avg_score | score_117 | score_116 | score_115 | party | state | year | role |
|---|---|---|---|---|---|---|---|---|---|
| Frank Pallone Jr. | 10000 | 98.67 | 96 | 100 | 100 | D | NJ | 2022 | Representative |
| Cathy McMorris Rodgers | 10000 | 0.00 | 0 | 0 | 0 | R | WA | 2022 | Representative |
| Gus Bilirakis | 5000 | 0.00 | 0 | 0 | 0 | R | FL | 2022 | Representative |
| Rodney Davis | 5000 | 11.00 | 26 | 7 | 0 | R | IL | 2022 | Representative |
| David McKinley | 5000 | 2.00 | 6 | 0 | 0 | R | WV | 2022 | Representative |
| Nancy Pelosi | 5000 | 100.00 | 100 | 100 | 100 | D | CA | 2022 | Representative |
| Mikie Sherrill | 5000 | 100.00 | 100 | 100 | NA | D | NJ | 2022 | Representative |
| Mike Thompson | 5000 | 100.00 | 100 | 100 | 100 | D | CA | 2022 | Representative |
| Brett Guthrie | 4500 | 0.00 | 0 | 0 | 0 | R | KY | 2022 | Representative |
| Anna Eshoo | 4000 | 99.33 | 98 | 100 | 100 | D | CA | 2022 | Representative |
| Suzanne Bonamici | 3500 | 100.00 | 100 | 100 | 100 | D | OR | 2022 | Representative |
| Joseph Neguse | 3500 | 100.00 | 100 | 100 | NA | D | CO | 2022 | Representative |
| Lisa Blunt Rochester | 3500 | 98.00 | 100 | 100 | 94 | D | DE | 2022 | Representative |
| Bill Johnson | 3000 | 0.00 | 0 | 0 | 0 | R | OH | 2022 | Representative |
| Cindy Axne | 2500 | 96.50 | 96 | 97 | NA | D | IA | 2022 | Representative |
| Larry Bucshon | 2500 | 0.00 | 0 | 0 | 0 | R | IN | 2022 | Representative |
| G K Butterfield | 2500 | 87.33 | 92 | 88 | 82 | D | NC | 2022 | Representative |
| Angie Craig | 2500 | 100.00 | 100 | 100 | NA | D | MN | 2022 | Representative |
| Josh Harder | 2500 | 97.00 | 94 | 100 | NA | D | CA | 2022 | Representative |
| Doris Matsui | 2500 | 100.00 | 100 | 100 | 100 | D | CA | 2022 | Representative |
| Lauren A Underwood | 2500 | 98.50 | 100 | 97 | NA | D | IL | 2022 | Representative |
| Alma Adams | 1000 | 98.00 | 94 | 100 | 100 | D | NC | 2022 | Representative |
| Pete Aguilar | 1000 | 100.00 | 100 | 100 | 100 | D | CA | 2022 | Representative |
| Kelly Armstrong | 1000 | 4.00 | 8 | 0 | NA | R | ND | 2022 | Representative |
| Troy Balderson | 1000 | 6.50 | 6 | 7 | NA | R | OH | 2022 | Representative |
| Karen Bass | 1000 | 100.00 | 100 | 100 | 100 | D | CA | 2022 | Representative |
| Jaime Herrera Beutler | 1000 | 11.00 | 10 | 8 | 15 | R | WA | 2022 | Representative |
| Earl Blumenauer | 1000 | 100.00 | 100 | 100 | 100 | D | OR | 2022 | Representative |
| Brendan Boyle | 1000 | 100.00 | 100 | 100 | 100 | D | PA | 2022 | Representative |
| Michael Burgess | 1000 | 0.00 | 0 | 0 | 0 | R | TX | 2022 | Representative |
| Liz Cheney | 1000 | 7.67 | 23 | 0 | 0 | R | WY | 2022 | Representative |
| Yvette D Clarke | 1000 | 100.00 | 100 | 100 | 100 | D | NY | 2022 | Representative |
| Tom Cole | 1000 | 8.33 | 18 | 7 | 0 | R | OK | 2022 | Representative |
| Joe Courtney | 1000 | 100.00 | 100 | 100 | 100 | D | CT | 2022 | Representative |
| John Curtis | 1000 | 2.67 | 8 | 0 | 0 | R | UT | 2022 | Representative |
| Diana DeGette | 1000 | 100.00 | 100 | 100 | 100 | D | CO | 2022 | Representative |
| Suzan DelBene | 1000 | 100.00 | 100 | 100 | 100 | D | WA | 2022 | Representative |
| Mark DeSaulnier | 1000 | 100.00 | 100 | 100 | 100 | D | CA | 2022 | Representative |
| Debbie Dingell | 1000 | 100.00 | 100 | 100 | 100 | D | MI | 2022 | Representative |
| Drew Ferguson | 1000 | 0.00 | 0 | 0 | 0 | R | GA | 2022 | Representative |
| Jimmy Gomez | 1000 | 100.00 | 100 | 100 | 100 | D | CA | 2022 | Representative |
| Kevin Hern | 1000 | 0.00 | 0 | 0 | NA | R | OK | 2022 | Representative |
| David Joyce | 1000 | 17.00 | 26 | 7 | 18 | R | OH | 2022 | Representative |
| Robin Kelly | 1000 | 100.00 | 100 | 100 | 100 | D | IL | 2022 | Representative |
| Dan Kildee | 1000 | 100.00 | 100 | 100 | 100 | D | MI | 2022 | Representative |
| Derek Kilmer | 1000 | 100.00 | 100 | 100 | 100 | D | WA | 2022 | Representative |
| Ann Kuster | 1000 | 100.00 | 100 | 100 | 100 | D | NH | 2022 | Representative |
| Darin LaHood | 1000 | 0.00 | 0 | 0 | 0 | R | IL | 2022 | Representative |
| Bob Latta | 1000 | 0.00 | 0 | 0 | 0 | R | OH | 2022 | Representative |
| Lucy McBath | 1000 | 100.00 | 100 | 100 | NA | D | GA | 2022 | Representative |
| Chris Pappas | 1000 | 100.00 | 100 | 100 | NA | D | NH | 2022 | Representative |
| Mark Pocan | 1000 | 100.00 | 100 | 100 | 100 | D | WI | 2022 | Representative |
| Jan Schakowsky | 1000 | 100.00 | 100 | 100 | 100 | D | IL | 2022 | Representative |
| Bobby Scott | 1000 | 96.00 | 94 | 97 | 97 | D | VA | 2022 | Representative |
| Terri A Sewell | 1000 | 92.67 | 96 | 97 | 85 | D | AL | 2022 | Representative |
| Adrian Smith | 1000 | 0.00 | 0 | 0 | 0 | R | NE | 2022 | Representative |
| Pete Stauber | 1000 | 6.50 | 6 | 7 | NA | R | MN | 2022 | Representative |
| Tim Walberg | 1000 | 0.00 | 0 | 0 | 0 | R | MI | 2022 | Representative |
| Kevin Brady | 0 | 0.67 | 2 | 0 | 0 | R | TX | 2022 | Representative |
paste("The total amount of money ASHA-PAC donated to Representatives' 2022 Campaigns:", sum(asha_2022_rep$ASHA_PAC_total), "dollars.")## [1] "The total amount of money ASHA-PAC donated to Representatives' 2022 Campaigns: 126500 dollars."Money by Most Recent HRC Rating
Splitting the score_117 into 0, 1-25, 26-50, 51-75, and 75-100 and see how much money is spent.
asha_2022_rep <- asha_2022_rep %>%
mutate(groups_117 = case_when(
score_117 == 0 ~ "0",
score_117 <= 25 ~ "1-25",
score_117 <= 50 ~ "26-50",
score_117 <= 75 ~ "51-75",
score_117 <= 100 ~ "76-100"
),
groups_116 = case_when(
score_116 == 0 ~ "0",
score_116 <= 25 ~ "1-25",
score_116 <= 50 ~ "26-50",
score_116 <= 75 ~ "51-75",
score_116 <= 100 ~ "76-100"))
asha_2022_rep %>%
group_by(groups_117) %>%
summarise(n = n(), money_spent = sum(ASHA_PAC_total),
percent_spent = round((money_spent/126500)*100,2)) %>%
as_tibble() %>% rmarkdown::paged_table()ASHA-PAC donated:
$32,000 to campaigns given an HRC score of 0 (over 25% of the total money spent)
$12,000 to campaigns given an HRC score of less than 25
$6,000 to campaigns given an HRC score between 26-50
$76,500 to campaigns given an HRC score above 75
That’s $50,000 to campaigns with a score less than 50, or ~40% of their campaign donations.
# asha_2022_rep %>%
# ggplot(aes(groups_117, ASHA_PAC_total)) + geom_bar(stat = "identity")
# asha_2022_rep %>%
# ggplot(aes(groups_117, ASHA_PAC_total)) +
# geom_boxplot(width = .5, outlier.shape = NA) +
# geom_jitter(width = .1, alpha = .5)+
# theme_minimal() +
# labs(x = "HRC Rating in 25pt intervals",
# y = "Inidivudal Campaign Donations Amount ($)",
# title = "ASHA-PAC's 2022 Representative Campaign Donations")+
# theme(plot.title = element_text(hjust = .5))Money by Average HRC Rating
Splitting the avg_score into 0, 1-25, 26-50, 51-75, and 75-100 and see how much money is spent.
asha_2022_rep <- asha_2022_rep %>%
mutate(groups_avg = case_when(
avg_score == 0 ~ "0",
avg_score <= 25 ~ "1-25",
avg_score <= 50 ~ "26-50",
avg_score <= 75 ~ "51-75",
avg_score <= 100 ~ "76-100"
))
asha_2022_rep %>%
group_by(groups_avg) %>%
summarise(n = n(), money_spent = sum(ASHA_PAC_total),
percent_spent = round((money_spent/126500)*100,2)) %>%
as_tibble() %>% rmarkdown::paged_table()Based on Representatives’ average score over the the most recent congressional sessions
ASHA-PAC donated:
$32,000 to campaigns given an HRC score of 0 (over 25% of the total money spent)
$18,000 to campaigns given an HRC score of less than 25
$76,500 to campaigns given an HRC score above 75
That’s 50,000 dollars to campaigns with an HRC equality score \(\leq\) 50, or ~40% of their campaign donations.
2020
Senators
Senators who won their race, their HRC scores, and the amount that ASHA-PAC donated to their 2020 campaigns.
ashaPAC %>% select(name, party, state, ASHA_PAC_total, year, role) %>%
filter(year == "2020", role == "Senator") %>%
left_join(hrc_sen_df %>%
select(name,avg_score, score_117:score_115), by = "name") -> asha_2020_sen
asha_2020_sen <- asha_2020_sen %>%
mutate(score_116 = case_when(
name == "Joe Kennedy III" ~ 100,
TRUE ~ score_116),
score_115 = case_when(
name == "Joe Kennedy III" ~ 100,
TRUE ~ score_115
),
avg_score = case_when(
name == "Joe Kennedy III" ~ 100,
TRUE ~ avg_score
))
asha_2020_sen %>%
arrange(desc(ASHA_PAC_total)) %>%
relocate(name, ASHA_PAC_total, avg_score:score_115)%>%
kbl(table.attr = "style = \"color: black;\"") %>%
kable_styling(bootstrap_options = c("striped", "hover"), position = "center") %>%
scroll_box(height = "400px")| name | ASHA_PAC_total | avg_score | score_117 | score_116 | score_115 | party | state | year | role |
|---|---|---|---|---|---|---|---|---|---|
| Bill Cassidy | 5000 | 4.00 | 12 | 0 | 0 | R | LA | 2020 | Senator |
| Susan Collins | 3500 | 46.00 | 54 | 51 | 33 | R | ME | 2020 | Senator |
| Jeanne Shaheen | 3500 | 100.00 | 100 | 100 | 100 | D | NH | 2020 | Senator |
| Dick Durbin | 2500 | 100.00 | 100 | 100 | 100 | D | IL | 2020 | Senator |
| Cindy Hyde-Smith | 2500 | 2.50 | 5 | 0 | NA | R | MS | 2020 | Senator |
| Ben Luján | 2500 | 100.00 | 100 | NA | NA | D | NM | 2020 | Senator |
| Mark Warner | 2500 | 92.00 | 94 | 91 | 91 | D | VA | 2020 | Senator |
| Jeff Merkley | 2000 | 100.00 | 100 | 100 | 100 | D | OR | 2020 | Senator |
| Shelley Moore Capito | 1500 | 6.33 | 19 | 0 | 0 | R | WV | 2020 | Senator |
| Joe Kennedy III | 1000 | 100.00 | NA | 100 | 100 | D | MA | 2020 | Senator |
| Tina Smith | 1000 | 100.00 | 100 | 100 | 100 | D | MN | 2020 | Senator |
Total $ Donated
paste("The total amount of money ASHA-PAC donated to Senators' 2020 Campaigns:", sum(asha_2020_sen$ASHA_PAC_total), "dollars.")## [1] "The total amount of money ASHA-PAC donated to Senators' 2020 Campaigns: 27500 dollars."Money by Most Recent HRC Rating
asha_2020_sen <- asha_2020_sen %>%
mutate(groups_117 = case_when(
score_117 == 0 ~ "0",
score_117 <= 25 ~ "1-25",
score_117 <= 50 ~ "26-50",
score_117 <= 75 ~ "51-75",
score_117 <= 100 ~ "76-100"
),
groups_116 = case_when(
score_116 == 0 ~ "0",
score_116 <= 25 ~ "1-25",
score_116 <= 50 ~ "26-50",
score_116 <= 75 ~ "51-75",
score_116 <= 100 ~ "76-100"))
asha_2020_sen %>%
group_by(groups_117) %>%
summarise(n = n(), money_spent = sum(ASHA_PAC_total),
percent_spent = round((money_spent/27500)*100,2)) %>%
as_tibble() %>% rmarkdown::paged_table()Based on Senators’ most recent HRC equality score
In 2020, ASHA-PAC donated:
$9,000 to campaigns given an HRC score less than 25
$3,500 to campaigns given an HRC score between 51-75
$14,000 to campaigns given an HRC score above 75
Approximately a third of their donations went towards candidates with a score of less than 25.
Money by Average HRC Rating
asha_2020_sen <- asha_2020_sen %>%
mutate(groups_avg = case_when(
avg_score == 0 ~ "0",
avg_score <= 25 ~ "1-25",
avg_score <= 50 ~ "26-50",
avg_score <= 75 ~ "51-75",
avg_score <= 100 ~ "76-100"
))
asha_2020_sen %>%
group_by(groups_avg) %>%
summarise(n = n(), money_spent = sum(ASHA_PAC_total),
percent_spent = round((money_spent/27500)*100,2)) %>%
as_tibble() %>% rmarkdown::paged_table()Based on Senators’ average HRC equality score
In 2020, ASHA-PAC donated:
$9,000 to campaigns given an HRC score less than 25
$3,500 to campaigns given an HRC score between 51-75
$15,000 to campaigns given an HRC score above 75
Approximately a third of their donations went towards candidates with an average score \(\leq\) 25.
Approximately 45% of their donations went towards candidates with an average score \(\leq\) 50.
Representatives
Representatives who won their race, their HRC scores, and the amount that ASHA-PAC donated to their 2020 campaigns.
ashaPAC %>% select(name, party, state, ASHA_PAC_total, year, role) %>%
filter(year == "2020", role == "Representative") %>%
left_join(hrc_rep_df %>%
select(name,avg_score, score_117:score_115), by = "name") -> asha_2020_rep
#asha_2020_rep %>% filter(is.na(score_116))
asha_2020_rep <- asha_2020_rep %>%
mutate(
score_116 = case_when(
name == "Donna Shalala" ~ 100, #na for 115
name == "Nita M Lowey" ~ 100, #100 for 115
name == "Greg Walden" ~ 47, #0 for 115
name == "Kendra Horn" ~ 88, #NA for 115
name == "Anthony Brindisi" ~ 88, #NA for 115
name == "George Holding" ~ 0, # 0 for 115
#name == "John Lewis" ~
name == "Pete Olson" ~ 7, #0 for 115
name == "Tom Reed" ~ 47, #43 for 115
name == "Steve Stivers" ~ 28, #0 for 115,
TRUE ~ score_116
),
score_115 = case_when(
# name == "Donna Shalala" ~ 100, #na for 115
name == "Nita M Lowey" ~ 100, #100 for 115
name == "Greg Walden" ~ 0, #0 for 115
# name == "Kendra Horn" ~ 88, #NA for 115
# name == "Anthony Brindisi" ~ 88, #NA for 115
name == "George Holding" ~ 0, # 0 for 115
#name == "John Lewis" ~
name == "Pete Olson" ~ 0, #0 for 115
name == "Tom Reed" ~ 43, #43 for 115
name == "Steve Stivers" ~ 0, #0 for 115,
TRUE ~ score_115)
)
#Need to recalculate the average scores since I manually added in the above scores
asha_2020_rep %>%
select(name, score_117:score_115) %>% #keep only relevant info
pivot_longer(score_117:score_115, names_to = "session", values_to = "score") %>%
group_by(name) %>%
summarise(name = name,
avg_score = round(mean(score, na.rm = T),2)) %>%
unique() %>%
ungroup() %>%
right_join(asha_2020_rep %>% select(-avg_score), by = "name") -> asha_2020_repasha_2020_rep %>%
arrange(desc(ASHA_PAC_total)) %>%
relocate(name, ASHA_PAC_total, avg_score, score_117:score_115)%>%
kbl(table.attr = "style = \"color: black;\"") %>%
kable_styling(bootstrap_options = c("striped", "hover"), position = "center") %>%
scroll_box(height = "400px")| name | ASHA_PAC_total | avg_score | score_117 | score_116 | score_115 | party | state | year | role |
|---|---|---|---|---|---|---|---|---|---|
| David McKinley | 10000 | 2.00 | 6 | 0 | 0 | R | WV | 2020 | Representative |
| Frank Pallone Jr. | 10000 | 98.67 | 96 | 100 | 100 | D | NJ | 2020 | Representative |
| Gus Bilirakis | 10000 | 0.00 | 0 | 0 | 0 | R | FL | 2020 | Representative |
| Mike Thompson | 10000 | 100.00 | 100 | 100 | 100 | D | CA | 2020 | Representative |
| Richard E Neal | 10000 | 99.33 | 98 | 100 | 100 | D | MA | 2020 | Representative |
| Kevin Brady | 7500 | 0.67 | 2 | 0 | 0 | R | TX | 2020 | Representative |
| Brett Guthrie | 6000 | 0.00 | 0 | 0 | 0 | R | KY | 2020 | Representative |
| Cathy McMorris Rodgers | 6000 | 0.00 | 0 | 0 | 0 | R | WA | 2020 | Representative |
| Anna Eshoo | 5000 | 99.33 | 98 | 100 | 100 | D | CA | 2020 | Representative |
| Bobby L Rush | 5000 | 98.67 | 96 | 100 | 100 | D | IL | 2020 | Representative |
| Bobby Scott | 5000 | 96.00 | 94 | 97 | 97 | D | VA | 2020 | Representative |
| Doris Matsui | 5000 | 100.00 | 100 | 100 | 100 | D | CA | 2020 | Representative |
| Glenn Thompson | 5000 | 0.00 | 0 | 0 | 0 | R | PA | 2020 | Representative |
| Lloyd Doggett | 5000 | 100.00 | 100 | 100 | 100 | D | TX | 2020 | Representative |
| Nancy Pelosi | 5000 | 100.00 | 100 | 100 | 100 | D | CA | 2020 | Representative |
| Paul Tonko | 5000 | 100.00 | 100 | 100 | 100 | D | NY | 2020 | Representative |
| Ron Kind | 5000 | 99.33 | 98 | 100 | 100 | D | WI | 2020 | Representative |
| Virginia Foxx | 5000 | 0.00 | 0 | 0 | 0 | R | NC | 2020 | Representative |
| Lisa Blunt Rochester | 4500 | 98.00 | 100 | 100 | 94 | D | DE | 2020 | Representative |
| Donna Shalala | 4000 | 100.00 | NA | 100 | NA | D | FL | 2020 | Representative |
| Rosa DeLauro | 4000 | 99.33 | 98 | 100 | 100 | D | CT | 2020 | Representative |
| Lauren A Underwood | 3500 | 98.50 | 100 | 97 | NA | D | IL | 2020 | Representative |
| Peter Welch | 3500 | 100.00 | 100 | 100 | 100 | D | VT | 2020 | Representative |
| Suzanne Bonamici | 3500 | 100.00 | 100 | 100 | 100 | D | OR | 2020 | Representative |
| Tim Walberg | 3500 | 0.00 | 0 | 0 | 0 | R | MI | 2020 | Representative |
| Bill Johnson | 3000 | 0.00 | 0 | 0 | 0 | R | OH | 2020 | Representative |
| Debbie Dingell | 2500 | 100.00 | 100 | 100 | 100 | D | MI | 2020 | Representative |
| Diana DeGette | 2500 | 100.00 | 100 | 100 | 100 | D | CO | 2020 | Representative |
| Earl Blumenauer | 2500 | 100.00 | 100 | 100 | 100 | D | OR | 2020 | Representative |
| Elise Stefanik | 2500 | 31.33 | 8 | 40 | 46 | R | NY | 2020 | Representative |
| Fred Upton | 2500 | 38.33 | 58 | 42 | 15 | R | MI | 2020 | Representative |
| G K Butterfield | 2500 | 87.33 | 92 | 88 | 82 | D | NC | 2020 | Representative |
| Greg Walden | 2500 | 23.50 | NA | 47 | 0 | R | OR | 2020 | Representative |
| Kevin McCarthy | 2500 | 0.00 | 0 | 0 | 0 | R | CA | 2020 | Representative |
| Michael Burgess | 2500 | 0.00 | 0 | 0 | 0 | R | TX | 2020 | Representative |
| Nita M Lowey | 2500 | 100.00 | NA | 100 | 100 | D | NY | 2020 | Representative |
| Robin Kelly | 2500 | 100.00 | 100 | 100 | 100 | D | IL | 2020 | Representative |
| Steny H Hoyer | 2500 | 99.00 | 100 | 100 | 97 | D | MD | 2020 | Representative |
| Steve Scalise | 2500 | 0.00 | 0 | 0 | 0 | R | NA | 2020 | Representative |
| Tom Rice | 2500 | 6.67 | 20 | 0 | 0 | R | SC | 2020 | Representative |
| Angie Craig | 2000 | 100.00 | 100 | 100 | NA | D | MN | 2020 | Representative |
| Cheri Bustos | 2000 | 100.00 | 100 | 100 | 100 | D | IL | 2020 | Representative |
| David Joyce | 2000 | 17.00 | 26 | 7 | 18 | R | OH | 2020 | Representative |
| Jan Schakowsky | 2000 | 100.00 | 100 | 100 | 100 | D | IL | 2020 | Representative |
| Jason Smith | 2000 | 0.00 | 0 | 0 | 0 | R | MO | 2020 | Representative |
| John Larson | 2000 | 100.00 | 100 | 100 | 100 | D | CT | 2020 | Representative |
| Kendra Horn | 2000 | 88.00 | NA | 88 | NA | D | OK | 2020 | Representative |
| Kurt Schrader | 2000 | 99.00 | 100 | 97 | 100 | D | OR | 2020 | Representative |
| Rodney Davis | 2000 | 11.00 | 26 | 7 | 0 | R | IL | 2020 | Representative |
| Steven Horsford | 1500 | 100.00 | 100 | 100 | NA | D | NV | 2020 | Representative |
| Adrian Smith | 1000 | 0.00 | 0 | 0 | 0 | R | NE | 2020 | Representative |
| Alma Adams | 1000 | 98.00 | 94 | 100 | 100 | D | NC | 2020 | Representative |
| Ann Kuster | 1000 | 100.00 | 100 | 100 | 100 | D | NH | 2020 | Representative |
| Anthony Brindisi | 1000 | 88.00 | NA | 88 | NA | D | NY | 2020 | Representative |
| Bill Pascrell Jr. | 1000 | 100.00 | 100 | 100 | 100 | D | NJ | 2020 | Representative |
| Billy Long | 1000 | 0.00 | 0 | 0 | 0 | R | MO | 2020 | Representative |
| Bob Latta | 1000 | 0.00 | 0 | 0 | 0 | R | OH | 2020 | Representative |
| Brad Schneider | 1000 | 100.00 | 100 | 100 | 100 | D | IL | 2020 | Representative |
| Brendan Boyle | 1000 | 100.00 | 100 | 100 | 100 | D | PA | 2020 | Representative |
| Brian Fitzpatrick | 1000 | 66.67 | 68 | 71 | 61 | R | PA | 2020 | Representative |
| Brian Higgins | 1000 | 100.00 | 100 | 100 | 100 | D | NY | 2020 | Representative |
| Buddy Carter | 1000 | 0.00 | 0 | 0 | 0 | R | GA | 2020 | Representative |
| Chris Pappas | 1000 | 100.00 | 100 | 100 | NA | D | NH | 2020 | Representative |
| Cindy Axne | 1000 | 96.50 | 96 | 97 | NA | D | IA | 2020 | Representative |
| Dan Kildee | 1000 | 100.00 | 100 | 100 | 100 | D | MI | 2020 | Representative |
| Danny K Davis | 1000 | 95.00 | 100 | 97 | 88 | D | IL | 2020 | Representative |
| Darin LaHood | 1000 | 0.00 | 0 | 0 | 0 | R | IL | 2020 | Representative |
| Derek Kilmer | 1000 | 100.00 | 100 | 100 | 100 | D | WA | 2020 | Representative |
| Donald McEachin | 1000 | 100.00 | 100 | 100 | 100 | D | VA | 2020 | Representative |
| Drew Ferguson | 1000 | 0.00 | 0 | 0 | 0 | R | GA | 2020 | Representative |
| Dwight Evans | 1000 | 100.00 | 100 | 100 | 100 | D | PA | 2020 | Representative |
| French Hill | 1000 | 0.00 | 0 | 0 | 0 | R | AR | 2020 | Representative |
| George Holding | 1000 | 0.00 | NA | 0 | 0 | R | NC | 2020 | Representative |
| Hakeem Jeffries | 1000 | 100.00 | 100 | 100 | 100 | D | NY | 2020 | Representative |
| Jahana Hayes | 1000 | 100.00 | 100 | 100 | NA | D | CT | 2020 | Representative |
| Jaime Herrera Beutler | 1000 | 11.00 | 10 | 8 | 15 | R | WA | 2020 | Representative |
| James E Clyburn | 1000 | 95.00 | 100 | 100 | 85 | D | SC | 2020 | Representative |
| Jared Huffman | 1000 | 100.00 | 100 | 100 | 100 | D | CA | 2020 | Representative |
| Jimmy Gomez | 1000 | 100.00 | 100 | 100 | 100 | D | CA | 2020 | Representative |
| Jimmy Panetta | 1000 | 100.00 | 100 | 100 | 100 | D | CA | 2020 | Representative |
| Joe Courtney | 1000 | 100.00 | 100 | 100 | 100 | D | CT | 2020 | Representative |
| John Lewis | 1000 | NaN | NA | NA | NA | D | GA | 2020 | Representative |
| Joseph Neguse | 1000 | 100.00 | 100 | 100 | NA | D | CO | 2020 | Representative |
| Josh Harder | 1000 | 97.00 | 94 | 100 | NA | D | CA | 2020 | Representative |
| Judy Chu | 1000 | 100.00 | 100 | 100 | 100 | D | CA | 2020 | Representative |
| Karen Bass | 1000 | 100.00 | 100 | 100 | 100 | D | CA | 2020 | Representative |
| Katherine Clark | 1000 | 100.00 | 100 | 100 | 100 | D | MA | 2020 | Representative |
| Kathy Castor | 1000 | 98.00 | 94 | 100 | 100 | D | FL | 2020 | Representative |
| Kim Schrier | 1000 | 100.00 | 100 | 100 | NA | D | WA | 2020 | Representative |
| Linda Sánchez | 1000 | 100.00 | 100 | 100 | 100 | D | CA | 2020 | Representative |
| Lucille Roybal-Allard | 1000 | 100.00 | 100 | 100 | 100 | D | CA | 2020 | Representative |
| Lucy McBath | 1000 | 100.00 | 100 | 100 | NA | D | GA | 2020 | Representative |
| Mark DeSaulnier | 1000 | 100.00 | 100 | 100 | 100 | D | CA | 2020 | Representative |
| Mark Pocan | 1000 | 100.00 | 100 | 100 | 100 | D | WI | 2020 | Representative |
| Matt Cartwright | 1000 | 100.00 | 100 | 100 | 100 | D | PA | 2020 | Representative |
| Mike Doyle | 1000 | 100.00 | 100 | 100 | 100 | D | PA | 2020 | Representative |
| Mike Kelly | 1000 | 0.00 | 0 | 0 | 0 | R | PA | 2020 | Representative |
| Nanette Barragán | 1000 | 100.00 | 100 | 100 | 100 | D | CA | 2020 | Representative |
| Pete Olson | 1000 | 3.50 | NA | 7 | 0 | R | TX | 2020 | Representative |
| Pete Stauber | 1000 | 6.50 | 6 | 7 | NA | R | MN | 2020 | Representative |
| Raul Ruiz | 1000 | 100.00 | 100 | 100 | 100 | D | CA | 2020 | Representative |
| Steve Stivers | 1000 | 14.00 | NA | 28 | 0 | R | OH | 2020 | Representative |
| Suzan DelBene | 1000 | 100.00 | 100 | 100 | 100 | D | WA | 2020 | Representative |
| Terri A Sewell | 1000 | 92.67 | 96 | 97 | 85 | D | AL | 2020 | Representative |
| Tom Cole | 1000 | 8.33 | 18 | 7 | 0 | R | OK | 2020 | Representative |
| Tom O’Halleran | 1000 | 99.00 | 100 | 97 | 100 | D | AZ | 2020 | Representative |
| Tom Reed | 1000 | 45.00 | NA | 47 | 43 | R | NY | 2020 | Representative |
| Tom Suozzi | 1000 | 100.00 | 100 | 100 | 100 | D | NY | 2020 | Representative |
| Tony Cárdenas | 1000 | 99.33 | 98 | 100 | 100 | D | CA | 2020 | Representative |
| Troy Balderson | 1000 | 6.50 | 6 | 7 | NA | R | OH | 2020 | Representative |
| Vernon Buchanan | 1000 | 4.33 | 6 | 7 | 0 | R | FL | 2020 | Representative |
| Yvette D Clarke | 1000 | 100.00 | 100 | 100 | 100 | D | NY | 2020 | Representative |
Total $ Donated
paste("The total amount of money ASHA-PAC donated to Representatives' 2020 Campaigns:", sum(asha_2020_rep$ASHA_PAC_total), "dollars.")## [1] "The total amount of money ASHA-PAC donated to Representatives' 2020 Campaigns: 265500 dollars."Money by Most Recent HRC Rating
asha_2020_rep <- asha_2020_rep %>%
mutate(groups_117 = case_when(
score_117 == 0 ~ "0",
score_117 <= 25 ~ "1-25",
score_117 <= 50 ~ "26-50",
score_117 <= 75 ~ "51-75",
score_117 <= 100 ~ "76-100"
),
groups_116 = case_when(
score_116 == 0 ~ "0",
score_116 <= 25 ~ "1-25",
score_116 <= 50 ~ "26-50",
score_116 <= 75 ~ "51-75",
score_116 <= 100 ~ "76-100"
))
asha_2020_rep %>%
group_by(groups_117) %>%
summarise(n = n(), money_spent = sum(ASHA_PAC_total),
percent_spent = round((money_spent/265500)*100,2)) %>%
as_tibble() %>% rmarkdown::paged_table()In 2020, ASHA-PAC donated:
$56,000 to campaigns given an HRC score of 0 (over 21% of the total money spent)
$27,500 to campaigns given an HRC score of less than 25
$4,000 to campaigns given an HRC score between 26-50
$3,500 to campaigns given an HRC score between 51-75
$157,500 to campaigns given an HRC score above 75
$87,500 or approximately 32.96% of their total campaign spending went towards campaigns with a score of less than 50 in 2020.
Money by Average HRC Rating
asha_2020_rep <- asha_2020_rep %>%
mutate(groups_avg = case_when(
avg_score == 0 ~ "0",
avg_score <= 25 ~ "1-25",
avg_score <= 50 ~ "26-50",
avg_score <= 75 ~ "51-75",
avg_score <= 100 ~ "76-100"
))
asha_2020_rep %>%
group_by(groups_avg) %>%
summarise(n = n(), money_spent = sum(ASHA_PAC_total),
percent_spent = round((money_spent/265500)*100,2)) %>%
as_tibble() %>% rmarkdown::paged_table()Based on Representatives’ average most recent HRC equality scores
In 2020, ASHA-PAC donated:
$57,000 to campaigns given an HRC score of 0 (over 21% of the total money spent)
$33,500 to campaigns given an HRC score of less than 25
$6,000 to campaigns given an HRC score between 26-50
$1,00 to campaigns given an HRC score between 51-75
$167,00 to campaigns given an HRC score above 75
96,500 or approximately 36.35% of their total campaign spending went towards campaigns with an average HRC equality score \(\leq\) 50 in 2020.
ASHA-PAC Spending over time
# ashaPAC has money spent and year, and role
#Make a separate df to examine spending over time, extracting only relevant information and aligning variables to graph the data
asha_2020_rep %>%
mutate(year_chamber = "2020 HoR",
hrc_category = groups_116) %>%
select(name, party, state, ASHA_PAC_total, year_chamber, hrc_category) -> over_time
asha_2020_sen%>%
mutate(year_chamber = "2020 Senate",
hrc_category = groups_116) %>%
select(name, party, state, ASHA_PAC_total, year_chamber, hrc_category) %>%
bind_rows(over_time) -> over_time
asha_2022_rep%>%
mutate(year_chamber = "2022 HoR",
hrc_category = groups_117) %>%
select(name, party, state, ASHA_PAC_total, year_chamber, hrc_category) %>%
bind_rows(over_time) -> over_time
asha_2022_sen%>%
mutate(year_chamber = "2022 Senate",
hrc_category = groups_117) %>%
select(name, party, state, ASHA_PAC_total, year_chamber, hrc_category) %>%
bind_rows(over_time) -> over_timeStatic Plot
set.seed(20)
#graph spending
over_time %>%
filter(!is.na(hrc_category)) %>%
ggplot(aes(year_chamber, ASHA_PAC_total, color = hrc_category)) +
geom_jitter(width = .2,
height = 200,
alpha = .8,
size = 5)+
theme_minimal() +
theme(panel.background = element_rect(fill = "lightgray"))+
labs(x = "Year & Chamber of Congress",
y = "ASHA-PAC Campaign Contribution ($)") +
# scale_color_discrete(name = "HRC Score") +
scale_color_brewer(palette = "YlOrRd",
direction = -1,
name = "HRC Score")
Reactive Plot
set.seed(20)
#graph spending
p <- over_time %>%
filter(!is.na(hrc_category)) %>%
ggplot(aes(year_chamber, ASHA_PAC_total, color = hrc_category)) +
geom_jitter(width = .2,
height = 200,
alpha = .8,
size = 5)+
theme_minimal() +
theme(panel.background = element_rect(fill = "lightgray"))+
labs(x = "Year & Chamber of Congress",
y = "ASHA-PAC Campaign Contribution ($)") +
# scale_color_discrete(name = "HRC Score") +
scale_color_brewer(palette = "YlOrRd",
direction = -1,
name = "HRC Score")
ggplotly(p)